Studies on the Ship Basic Planning Support Systemusing Maritime Logistics Big Data
（海洋物流ビッグデータを利用した船舶基本計画支援システムに関する研究）

August 2021
Mohammad Danil Arifin
D145238TABLE OF CONTENTTABLE OF CONTENT………………………………………………………………… i
LIST OF TABLE………………………………………………………………..............vi
LIST OF FIGURE……………………………………………………………………..viii
Chapter 1. Introduction…………………………………………………………………..1
1.1

Present Situation of Marine Logistics Big Data………………………………..1

1.2

Ship Allocation Model Application…………………………………………….5

1.3

Objective of this Study………………………………………………………….9

1.4

Organization of this Dissertation……………………………………………….9

Chapter 2. Related Studies……………………………………………………………...12
2.1

2.2

Definition of Maritime Big Data………………………………………………12
2.1.1

Big Data in General Sense……………………………………………122.1.2

Big Data in Logistic and Transportation……………………………..14

2.1.3

Maritime Big Data in General………………………………………..16

The Studies of Maritime Big Data Application Area………………………….17
2.2.1

Maritime Big Data and E-Navigation………………………………...17

2.2.2

Maritime Big Data Innovation in the field of Weather Routing………18

2.2.3

Maritime Big Data Innovation in the field of Monitoring/Tracking….20

2.2.4

Maritime Big Data Application in Ports and Terminals………………21

2.2.5

Studies of Maritime Big Data for Safety Improvement………………25

2.2.6

Maritime Big Data Application by NYK…………………………….27

2.2.7

Maritime Big Data Application for Other Purposes………………….32

2.2.8

Maritime Big Data in this Studies……………………………………35

i

2.3

Characteristics of this Study…………………………………………………...36
2.3.1

Marine Logistic Databases (MLDB)…………………………………36

2.3.2

Ship Allocation Model……………………………………………….37

2.3.3

Importance of Bulk Carrier…………………………………………..38

Chapter 3. Basic Concept……………………………………………………………….44
3.1

3.2

Marine Logistic Database (MLDB)…………………………………………...44
3.1.1

MLDB Input Data……………………………………………………44

3.1.2

MLDB Structure .…………………………………………………….45

3.1.3

Error Cleaning………………………………………………………..45

Ship Allocation Model………………………………………………………...50
3.2.1

3.3

Configuration of ship allocation model………………………………53

Simulation Using Ship Allocation Model……………………………………..53

Chapter 4. Development of Marine Logistics Database………………………………..55
4.1

Definition of Marine Logistic Database (MLDB)…………………………….55

4.2

Development of MLDB……………………………………………………….56
4.2.1

Input of MLDB ………………………………………………………56

4.3

Data Structure of MLDB………………………………………………………70

4.4

Data Cleaning …………………………………………………………………70

4.5

4.4.1

Duplicated Data …………………………………………....………...71

4.4.2

Unrealistic Ship Movement Data………....…………………………71

4.4.3

Unrealistic Ship Draught Data……………………………………….72

Generating Cargo Information………………………………………………...72
4.5.1

Checking Data Reliability……………………………………………72

4.5.2

Estimating Cargo Type Using Port Data……………………………..73
ii

4.6

4.7

4.5.3

Estimating Cargo Type Using Ship Size……………………………..73

4.5.4

Estimating Cargo Volume…………………………………................75

Confirmation of Cargo Estimation of MLDB…………………………………76
4.6.1

Cargo Estimation from Australia-Brazil to Japan…………………….76

4.6.2

Cargo Estimation from Australia-Brazil to Korea……………………78

4.6.3

Cargo Estimation from Australia-Brazil to China……………………80

Data Findings of MLDB………………………………………………………83
4.7.1

Findings of Data Availability………………………………………...83

4.7.2

Findings of Cargo Estimation Results………………………………..87

Chapter 5. Shipper Model………………………...…………………………………….90
5.1

Development of Shipper Model ………………………………………….…...90
5.1.1

Overview of Shipper Model………………………………………….90

5.1.2

Method of Shipper Model...…...…………………………………......91

5.2

Result of Shipper Model………..……………………………………………..98

5.3

Evaluation of Shipper Model ....……………………………………………..103

5.4

5.3.1

Comparison with actual locations ………………………………….103

5.3.2

Consistency of the cluster results..………………………………….104

5.3.3

Comparison with actual ship operation.…………………………….107

Findings of Shipper Model…………..……………………………………….108

Chapter 6. Shipowner Model……………………...…………………………..……….112
6.1

6.2

Development of Shipowner Model……………….………………………….112
6.1.1

Method of Shipowner Model Using Deep Learning………………..112

6.1.2

Deep Learning in This Study…………………………………...…...114

Result of Shipowner Model………………………………………………….118
iii

6.3

Evaluation of Shipowner Model ....………………………………………….119
6.3.1

Comparison using response surface method ……………………….119

6.3.2

Comparison using Threshold of the Estimation…………………….120

Chapter 7. Operator Model…………………………….………………………………121
7.1

Development of Operator Model……………….………...………………….121

7.2

Evaluation of Ship Allocation Model…………………………….………….125
7.2.1

Problem definition ...…….………………………………………….125

7.2.2

Simulation results ...……...………………………………………....125

7.2.3

Discussions ...……………....…………………………………........ 125

Chapter 8. Simulations………………………………………………………………...129
8.1

Case Studies………………………………………………………………….129

8.2

Examination of Supply-Demand Balance…………………………………...130

8.3

8.4

7.2.1

Problem definition…………………………………………………. 130

7.2.2

Simulation result……………………………………………………131

7.2.3

Discussions....………………………………………………………131

Examination of Supply-Demand Balance with Effective Ship Size…………132
8.3.1

Problem definition………………………………………………….132

8.3.2

Simulation results……………………………………………….….132

8.3.3

Additional simulations……………………………………………...133

Examination of Supply-Demand with Influence of Fuel Efficiency………...135
8.4.1

Problem definition…………………………………………………. 135

8.4.2

Simulation results ...………………………………………………...135

8.4.3

Discussion......……………………………………………………....136

iv

Chapter 9. Conclusions and Future Tasks………………………………………….….138
8.1

Conclusions ………………………………………………………………….138

8.2

Future Works.………………………….…………………………………….140

Bibliography……………………………………………………………………….….141
Acknowledgment……………………………………………………………………...159v

LIST OF TABLE
Table 2.1 Application area for big data in maritime industry……………………………32
Table 2.2 Dry bulk trade 2016-2017 (MT and % annual change……………………… 40
Table 2.3 Major dry bulks and steel: Producers and Users 2017………………………...41
Table 2.4 Major dry bulks and Iron Ore: Exporters and Importers 2017………………...41
Table 2.5 Major dry bulks and Coal: Exporters and Importers 2017…………………….41
Table 2.6 Major dry bulks and Grain: Exporters and Importers 2017…………………...42
Table 3.1 Input and output of ship allocation model…………………………………….52
Table 4.1 Typical Data of Ship Operation Data…………………………………………65
Table 4.2 Typical Data of Ship Data…………………………………………………….66
Table 4.3 Typical Data of Port Data…………………………………………………….67
Table 4.4 Typical Data of Route Data…………………………………………………..68
Table 4.5 Typical Data Trade from UN Comtrade……………………………………...69
Table 4.6 Example of Duplicated Data at Oita Port……………………………………71
Table 4.7 Bulk Carrier Status based on its Draught Rate………………………………73
Table 4.8 Estimation Cargo Type Using Port Data ……..………………………………73
Table 4.9 Result of Estimation Cargo 2014 (Australia~Japan) …………………………76
Table 4.10 Result of Estimation Cargo 2014 (Brazil~Japan) …………………………...77
Table 4.11 Result of Estimation Cargo 2014 (Australia~Korea) ………………………78
Table 4.12 Result of Estimation Cargo 2014 (Brazil~Korea) ………………………… 79
Table 4.13 Result of Estimation Cargo 2014 (Australia~China) ……………………….81
Table 4.14 Result of Estimation Cargo 2014 (Brazil-China) …………………………...82
Table 4.15 Availability Data of Developed Country……………………………………85
Table 4.16 Availability Data of Developing Country ………………………………….87
viTable 5.1 Example Data of Ship Operation from MLDB……………………………….91
Table 5.2 x.y coordinates of 6 points……………………………………………………93
Table 5.3 Euclidean distance of 6 points………………………………………………..94
Table 5.4 Cluster analysis process………………………………………………………97
Table 5.5 Amount of Iron Ore Imported from Australia (2014) ………………………..99
Table 5.6 Amount of Iron Ore Exported to Japan (2014) …………………………….102
Table 5.7 Shippers from Australia to Japan, 2014……………………………………..102
Table 5.8 Characteristic of shipper model based on Consignee point of view…….….107
Table 5.9 Characteristic of shipper model based on Shipper point of view…………..108
Table 5.10 Characteristic of shipper model based on port restriction…………………109
Table 5.11 Characteristic of shipper model based on a relation between shipper…..…110
Table 5.12 Deep learning hyperparameters……………………………………………111
Table 6.1 DL Input and Output Layer in Draught Rate Learning Model ….………….115
Table 6.2 DL Input and Output Layer in Average Voyage Speed Learning Model ….116
Table 6.3 DL Input and Output Layer in Port Staying Time Learning Model …….….117
Table 6.4 Deep learning hyperparameters ………………………………………….…118
Table 6.5 Example of prediction result by deep learning………………………………118
Table 6.6 Result of the shipper model…………………………………………………118
Table 6.7 Examples of estimation results by each owner using………….….…….….119
Table 6.8 Comparison of deep learning vs response surface………….….…………...119
Table 6.9 Comparison of deep learning vs response surface and threshold …………....120
Table 7.1 Ship allocation process…………….….…………………………………….124
Table 7.2 Port constraints...………………...………………………………………….127
Table 7.3 Typical ship specifications………………………………………………….127
vii

Table 8.1 Ship specification….…………………………….………………………… 132
Table 8.2 Results of ship distribution by efficiency increases 10%.......................……134
Table 8.3 Port constraints in Australia…………………………………………………135
Table 8.4 Replacement 210,000 DWT ships……………………………………......…135

viii

LIST OF FIGURE
Fig 1.1 Marine Digital ...................................................................................................... 3
Fig 1.2 Structure of the dissertation................................................................................ 11
Fig 2.1 Essence of “4Vs” as main difficulties of treating big data ................................. 13
Fig 2.2 Overview of the important features of big data ................................................. 14
Fig 2.3 System data flow ................................................................................................ 16
Fig 2.4 Critical points identified along a vessel trajectory ............................................. 26
Fig 2.5 Data flow chart in ship performance and navigation information ..................... 32
Fig 2.6 Big data in this study .......................................................................................... 35
Fig 2.7 MLDB ................................................................................................................ 37
Fig 2.8 Ship allocation model ......................................................................................... 38
Fig 2.9 World seaborne trade in cargo ton-miles, 2000-2018 (BTM)............................ 39
Fig 3.1 Basic concept of ship allocation ......................................................................... 52
Fig 4.1 Basic concept of MLDB ..................................................................................... 55
Fig 4.2 AIS Data Transponder ........................................................................................ 56
Fig 4.3 AIS Conceptual Operation Vies ....................................................................... 57
Fig 4.4 Mechan Ship AIS Diagram Block ...................................................................... 59
Fig 4.5 AIS Class A ........................................................................................................ 60
Fig 4.6 AIS Class B ........................................................................................................ 61
Fig 4.7 AIS Data Illustration .......................................................................................... 64
Fig 4.8 Illustration of Ship Data ..................................................................................... 66
Fig 4.9 Illustration of Port Data ...................................................................................... 67
Fig 4.10 Illustration of Route Data ................................................................................. 68ix

Fig 4.11 Illustration of Trade Data ................................................................................. 69
Fig 4.12 Data Structure of MLDB .................................................................................. 70
Fig 4.13 Bulk Carrier Draught Rate in Australia-Japan 2014 ........................................ 72
Fig 4.14 Estimating Cargo Using Port Data (Single Cargo) .......................................... 74
Fig 4.15 Estimating Cargo Using Port Size (Multi Cargo) ............................................ 74
Fig 4.16 Estimating Cargo Using Port Size (No Common Cargo) ................................ 75
Fig 4.17 Distribution of Draught Rate (Australia~Japan) .............................................. 77
Fig 4.18 Distribution of Draught Rate (Brazil~Japan) ................................................... 78
Fig 4.19 Distribution of Draught Rate (Australia~Korea) ............................................. 80
Fig 4.20 Distribution of Draught Rate (Brazil~Korea) .................................................. 80
Fig 4.21 Distribution of Draught Rate (Australia~China) .............................................. 81
Fig 4.22 Distribution of Draught Rate (Brazil~China)................................................... 82
Fig 4.23 Data Availability of Oita Port .......................................................................... 83
Fig 4.24 Data Availability of Newcastle Port ................................................................ 83
Fig 4.25 Data Availability of Busan Port ....................................................................... 84
Fig 4.26 Data Availability of Houston Port.................................................................... 84
Fig 4.27 Data Availability of Qingdao Port ................................................................... 84
Fig 4.28 Data Availability of Lumut Port ...................................................................... 85
Fig 4.29 Data Availability of Mumbai Port .................................................................... 85
Fig 4.30 Data Availability of Saigon Port ...................................................................... 86
Fig 4.31 Data Availability of Takoradi Port ................................................................... 86
Fig 4.32 Data Availability of Tanjung Priok Port .......................................................... 86
Fig 4.33 Cargo Estimation Results (Australia~Japan) ................................................... 87
Fig 4.34 Cargo Estimation Results (Australia~Korea) ................................................... 88xFig 4.35 Cargo Estimation Results (Australia~China) ................................................... 88
Fig 4.36 Cargo Estimation Results (Indonesia: Japan, Korea, China) ........................... 89
Fig 4.37 CargoEstimation Results (Vietnam: Japan, Korea, China) .............................. 89
Fig 5.1 Illustration of Shipper Model ............................................................................. 91
Fig 5.2 Set of 6 two-dimensional points ......................................................................... 93
Fig 5.3 Single link clustering of 6 two-dimensional points............................................ 94
Fig 5.4 Complete link clustering of 6 two-dimensional points ...................................... 95
Fig 5.5 Group average clustering of 6 two-dimensional points ..................................... 95
Fig 5.6 Ward’s clustering of 6 two-dimensional points ................................................. 96
Fig 5.7 Shippers Cluster Based on Consignee Point of View ........................................ 99
Fig 5.8 Correlation of port based on the Consignee point of view............................... 100
Fig 5.9 Shippers Cluster Based on Shipper Point of View........................................... 101
Fig 5.10 Correlation of port based on the Shipper point of view ................................. 101
Fig 5.11 Matching location of shipper based on Consignee point of view .................. 103
Fig 5.12 Matching location of shipper based on Shipper point of view....................... 104
Fig 5.13 Consistency of the cluster 2014 & 2017 based on Consignee point of view..105
Fig 5.14 Consistency of the cluster of 2014 ad 2017 based on Shipper point of view 106
Fig 5.15 Characteristics of Shipper (Consignee’s point of view) ................................ 108
Fig 5.16 Characteristics of Shipper (Shipper’s point of view) ..................................... 109
Fig 5.17 Characteristics of shipper based on a relationship between shippers............. 111
Fig 6.1 Structure of a deep learning neural network .................................................... 113
Fig 7.1 Illustration of Operator Model ......................................................................... 121
Fig 7.2 Comparison of actual and simulation results ................................................... 126
Fig 8.1 Illustration of the simulations........................................................................... 129
xiFig 8.2 Illustration of the supply-demand balance simulation ..................................... 130
Fig 8.3 Comparison of actual and simulation results ................................................... 131
Fig 8.4 Ship distribution by size ................................................................................... 133
Fig 8.5 Ship distribution when fuel efficiency increases by 10% ................................ 134
Fig 8.6 Comparison of simulation result 10% and 15% (S3) ....................................... 136

xii

Chapter 1
Introduction
1.1 Present Situation of Marine Logistics Big Data
Mobility and maritime transport have always played an important role in economic,
ecological, and social development [1]. With the development of international trade andglobalization, traffic and cargo volumes keep increasing. Therefore, numerous
stakeholders (such as maritime logistics companies, forwarders, agents, etc.) arecompelled to accept changes in the maritime transport sector and turn to more effective
practices by introducing technologies that can gather and process massive amounts of
information (in a cost-effective way) [2].
A large amount of data is generated from different sources and in different formats
in maritime transport on a daily basis. This includes traffic data, cargo data, weather data,
and machinery data [3]. Due to the size of the maritime transport network that includes
the aforementioned stakeholders, there exist large-scale planning problems at the strategic,
tactical, and operational levels [4]. Nowadays, Big Data analytics is applied to manyindustries (among which the maritime transport industry) to promote a better quality ofdecision-making processes [5].
According to Acharjya and Ahmed [6], “Big Data means more than just dealing witha large quantity of data. In general, it refers to the collection of large and complex datasets
which are difficult to process and analyze using traditional database management tools ordata processing applications”. When handling Big Data in large quantities, advanced
data-processing techniques and tools are required to effectively analyze and utilize thesedata [5].
Various stakeholders involved in cargo transports (from origin to destination) areconstrained to rely on the information provided by other parties involved. The more exact
and detailed such information, the smoother running of shipping operations. The level ofcooperation and coordination that is essential for the success of these businesses andcompetitiveness is based on information systems and information technology [7].
Decisions based on the information extracted from raw data can lead to several
advantages: increased safety and resource utilization, as well as a higher degree of
efficiency, sustainability, and environmental protection [8].
The concept of Big Data comes with a set of related components that enableorganizations to put the data to practical use and solve several business problems. These
include the IT infrastructure needed to support Big Data, the analytics applied to the data,technologies needed for Big Data projects, related skill sets, and the actual use cases thatmake sense for Big Data [9]. According to Nita and Mihailescu, the sources of data could
be communication, transport, social media, climate, search engines, GPS signals, online
shopping, mobile devices, and many others [10]. The sources of data can be the Internetof Things (IoT) as well, creating large data sets on a daily basis. IoT consists of various
devices, which are provided with digital sensors and connected for interacting. The
number of these devices, such as mobile phones or Radio Frequency Identification tags
(RFID) is rising rapidly [11], [12].
Even though there is no official definition of Big Data, the concept of Big Data goesbeyond the literal meaning of the data with great volume. Taylor-Sakry [13] defines Big
Data as “large sets of complex data, both structured and unstructured which traditional
processing techniques and/ or algorithms are unable to operate on.” This definition also2points out the importance of specific technology or methods, such as algorithms, to handle
Big Data
As the volume of marine big data has increased dramatically, there is significant
potential and high value hidden in the huge volumes of data that are widely used invarious fields, one of the main concerns is how to fully exploit the value of such data inthe development of marine logistics industries. There are several key uses of big data inthe shipping and logistics industry. Thanks to the use of big data engineering, the shipping
industry has grown even stronger over the past few years.

Fig 1.1 Marine Digital, [14]Big data is used to manage ship sensors and for predictive analysis, which is needed
to prevent delays and improve the overall operational efficiency of the industry. In the
shipping industry, proper cargo tracking is essential to ensure the necessary safety andconfidentiality. Through data obtained through proper tracking of shipments over several
years, information on the causes of vessel losses at sea, losses of containers inside oroutside terminals or warehouses, and other problems related to dispatch (for example, thereasons for damage to the goods) may be received. This big data for the shipping industry
can be used to make decisions in the future to predict and avoid costly problems, and tocreate more reliable cargo delivery options.
3

Everyone agrees that big data can play an important role in ship design. Basically,
this will be possible by analyzing the results obtained from the sensors of previously used
vessels. Data collected and analyzed over the life of the vessel will be useful for future
improvements in ship design. Previous datasets could help in testing the proposed ship
design without physically developing it. This is a very big advantage for the shipbuilding
industry.
As we move towards a more globalized economy, the demand for transportation of
goods and related logistical support will continue to grow exponentially. Over time, thisgrowth will increase the need for maximizing time and profitability to have the mostprofitable delivery processes. Through the use of advanced data processing techniques,
the delivery of goods will become more efficient. Improved transport services will
increase overall international trade.
Big Data which is effective for forecasting demand and planning process is the oil ofthe information economy [14]. The future competitiveness of marine industries will beaffected by how rapidly we take advantage of it. By identifying the real information
extracted from Big Data, we can take the advantage of its full value to help organizations
to be more efficient and profitable.
The other research found that Big Data has a big potential to improve operational
efficiency in shipping [15]. Furthermore, Big Data in marine industries is providing
information that can make a maritime operation more efficient [16]. Moreover, it is
widely believed that Big Data can aid in improving forecasts provided that we can analyze
and discover hidden patterns [17].
The global marine logistics industry has changed significantly because of theinfluence of the global movement of goods [18]. Hence, it is important to develop shipsthat meet specific needs and market requirements by developing the allocation model.
4Simultaneously, marine logistics big data can be acquired more easily than ever before(e.g., port, ship, route, international trade, and Automatic Identification System (AIS)
data) [19-23]. If these data are effectively utilized, great innovation might be achieved.

1.2 Ship Allocation Model Application
Maritime transport is characterized by a large number of stakeholders such as seaport
operators, shipbuilders and ship owners, agents, brokers, shipping and insurancecompanies or classification societies, etc. A large variety of stakeholders means a large
variety of business procedures and interests, among which there are various interests indata types. Thus, a clear definition of the term “Maritime Big Data” does not exist. In
dependence on the target, Maritime Big Data includes details of ships’ performance,
freight rates, weather data, labor costs, oil or even metal prices [24]. In addition, thenumber of digital sensors in maritime transport is also increasing. Consequently, thegeneration of Big Data in the maritime context is increasing, adding to the quantity of
data, and the data are provided by numerous different sources [25]. Besides weather
forecasts and historical data, the most important data resources in the case of voyage data
are provided by the bridge equipment to be recorded by the Voyage Data Recorder (VDR),
and by external monitoring such as the Automatic Identification System (AIS) [24], [25].
The main purpose of VDR is to provide data for analyses in case of accidents.
Therefore, VDR stores all recorded data of a voyage [25]. As the International Maritime
Organization (IMO) determines (Regulation 20, Chapter 5of the International Convention
for the Safety of Life at Sea – SOLAS, 1974), a VDR needs to be installed on all passenger
ships and other ships of 3000 gross tonnages or more on international voyages [26].
Information collected by VDR is date and time, ship’s position, speed, heading, bridge
audio, communication audio, wind speed and direction, main alarms on the bridge andrudder or engine orders and responses, and more [27]. According to Resolution
5MSC.333(90) (adopted on 22 May 2012), the time for which all stored data items are
retained should be at least 30 days/720 hours on the long-term recording medium and at
least 48 hours on fixed and float-free recording media. Data items older than this may beoverwritten with new data. A standard international voyage can last longer than 30 days
and data collected during this journey will be overwritten and not extracted and saved on
an external device. On short voyages, the data will also be deleted after the voyage or
during the upcoming voyage, making them inaccessible for a long-term analysis [24].
VDR collects a massive volume of data, providing deep insights into the voyage. As the
VDR data are overwritten after a specific period and replaced with new data, the data
should be sent ashore or be saved and exported manually or automatically. Mirović et al.
[25] claim that such information is still analyzed only in cases of accidents, and isotherwise deleted without consideration.
The AIS tracking system was originally developed as the collision avoidance toolthat enables commercial vessels to ‘see’ each other more clearly in any conditions and to
improve helmsman’s information about the surrounding environment. The AIS does thisby continuously transmitting the vessel’s position, identity, speed, and course, along withother relevant information, to all other AIS-equipped vessels within the range [28].
In this respect, the AIS enables safer and more efficient navigation by tracking all
ships within the range (enabling the exchange of ship data among ships and the shore)[29], [25]. AIS regulations are defined in the SOLAS as well and require AIStransponders to be installed on all passenger ships and all other ships of 300 gross
tonnages and more [30]. The AIS provides other ships and coastal authorities with static
data (IMO number, ship’s length, and beam, etc.), voyage related data (destination and
ETA, etc), dynamic data (position, course, speed, etc), and VTS data (short content
information related to various safety warnings and information on areas with warnings
about navigational and other dangers) [31], [25], [32]. Thus, it helps collision avoidanceand decision-making onboard in real-time [29]. In this respect, the AIS provides data as6

a basis for BDA in maritime transport. The frequency of the AIS data refreshing ratedepends on the vessel movement and navigational status, meaning that the data quantity
will increase as the vessel moves faster and/or alters the course.
However, the growing trend of the AIS has been such that in some most congested
waters the system is already overloaded as of today. Given the danger that this overload
can represent for the main mission of the AIS, that is collision avoidance, the International
Association of Marine Aids to Navigation and Lighthouse Authorities (IALA) and several
national maritime authorities have started the work on the VHF Data Exchange System(VDES). Rather than an evolution of AIS, VDES is a communications system
encompassing different subsystems of communications, one of them being the AIS [33],[34]. New techniques providing higher data rates than those used for the AIS are a coreelement of VDES. The VDES supports e-Navigation [33] and could have a significant
positive impact on the provision of maritime information services, such as maritime
safety information, general data communications at high data rates, locating, vessel traffic
management, satellite communications, etc. [35].
Maritime transport offers favorable conditions for Big Data Processes, by being
provided with a wide range of data from various sources and by being obliged to record
such data. As mentioned before, Big Data Analyses provide deeper insight into processes
and events and allow for well-informed decision making. In the same manner, as in otherindustries, the maritime industry might benefit from Big Data Analyses. In maritime
transport (as part of the maritime industry), innovations in BDA will bring advantages toefficient routing, operational planning, and safety improvements, as shown in thefollowing paragraphs.
There exist several current applications of Big Data in maritime transport and one ofthem is “operations”. In this respect, ship owners can determine the optimum speed forfuel consumption, taking into consideration factors such as bunker cost, freight rates, and7

schedules. Speed reduction will result in fuel consumption optimization and will alsosignificantly contribute to decreasing the emission of harmful gases, thus getting inconsonance with the development of ecological legislation [36].
Other applications of Big Data are voyage operations (vessels can be tracked using
dashboards instead of relying on notes, emails, or phone calls), tracking, etc. [37]. For
example, Freight Metrics provide the location visibility of a ship or vessels located arounda particular port. Freight Metrics has plotted locations of major shipping ports around
Australia on Google Maps. It enables a simplified connection to the port website forshipping schedules [38].
In Singapore and Malaysia, ports utilize Big Data techniques to create advanced
inspection systems to assess the history and cargo type of importers. The Hamburg Portin Germany uses a cloud-based analytics tool called SmartPortLogistics. The tool pulls
different types of data, such as vessel positions, height, and breadth of bridges, andplanned sailing routes. These data can be viewed in real-time on mobile applications [37].
Other studies also have applied Big Data to the maritime industry. For construction
applications, Hiekata et al proposed a high-accuracy block component measurement
method that uses point cloud data from a 3D laser scanner [39]. Aoyama et al proposed
new methods of extracting and utilizing monitoring data by introducing two additional
monitoring technologies and considering the reliability of each for advanced shipbuilding
construction management [40].
In the operations field, Perera et al analyzed large ship performance datasets topropose a model for evaluating ship performance under various seagoing conditions [41].
Ando et aland Yoshida et al proposed a data collection platform called the Ship
Information Management System and utilized the data collected for many purposes (e.g.,energy efficiency determinations, ship performance monitoring, and engine monitoring)8[42] [43]. Note that many of these studies have employed big data to improve ship
construction, operation, and performance; few have examined the use of big data for ship
demand prediction [44]. However, detailed predictions of the routes on which demand
will increase are currently difficult to execute. Therefore, a ship allocation model byharnessing marine logistic big data is conducted.

1.3 The objective of this Study
Based on the above considerations, we have been developing ship allocation models
using marine logistics data and its application to bulk carrier demand forecasting andbasic planning support
Following is the objective of this study:The support system of ship basic planning for bulk carrier

To realize the objective, there are three important points, Marine Logistics Database(MLDB), ship allocation model, and simulation. In the MLDB, integration of all of thedata i.e. AIS, ship, port, route, and trade data into a relational database is required.
Furthermore, in the ship allocation model, the development of three distinct models—theshipper, shipowner, and operator models is required. And then, in the simulation todevelop new ship allocation and examine which specification of the ship is effective,
several simulations are required.
The basic concepts of proposed methods and details of information regarding the
support system of ship basic planning are described in this study. Moreover, theeffectiveness of the developed models is discussed.

1.4 Organization of this Dissertation
The structure of this thesis is presented in Fig.1.1. The summary of each chapter isshown in the following.
9(Chapter 1) At the beginning of this thesis, a short overview of the present situationof marine logistics big data is described. In this chapter, the support system of ship basicplanning and ship allocation model concept is introduced. Furthermore, the objective andscope of the study are clarified.
(Chapter 2) In this chapter, the related studies of maritime big data applications aredescribed. Some references about maritime big data for several purposes are described.
Furthermore, the characteristics of this study are clarified. In addition, the differences ofthis study compare to the related studies are described.
(Chapter 3) The basic concept to develop a support system of ship basic planning is
introduced and discussed. This chapter consists of three important points, such as Marine
Logistics Database (MLDB), ship allocation model, and simulation(Chapter 4) The development of the Marine Logistics Database is defined andillustrated. Detailed information regarding the development of MLDB—input, data
structure, error cleaning, etc, are discussed. Furthermore, the evaluation of cargoestimation is described.
(Chapter 5) The development of the ship allocation model starting with the
explanation of the development of the shipper model. The results of the shipper model
are shown, and the evaluation of the model is discussed.
(Chapter 6). The development of the shipowner model is described. The results ofthe shipowner model are shown, and the evaluation of the model is discussed.
(Chapter 7). The development of the operator model is described in this section.
Results of the operator model are shown and the reproducibility of the proposed model isdiscussed.
10(Chapter 8). Several cases were simulated and performed. The cases; examinationof supply-demand balance, examination of effective ship size, and influence of fuelefficiency on demand are specifically discussed.
(Chapter 9) The last chapter presents the conclusions of the study and future tasks.

Chapter 1
Introduction

Chapter 2
Related Studies

Chapter 3
Basic Concept

Chapter 4
Marine Logistics
Database

Chapter 5
Shipper Model

Chapter 6
Shipowner Model

Chapter 7
Operator Model

Chapter 8
Simulations

Chapter 9Conclusions
Fig. 1.2 Structure of the dissertation

11

Chapter 2
Related Studies
2.1 Definition of Maritime Big Data
2.1.1 Big Data in General Sense
The term “big data” was created in the field of space engineering 18 years ago.
According to Press [45] and Friedman [46], the first use of the term “big data” appearedin 1997 in the work of Michael Cox and David Ellsworth who were scientists at NASA.
They defined big data to describe certain challenges which they were facing in the 1990s.
This “challenge” was massive information that was generated by supercomputers andcould not be processed and visualized by the technology of that time. Since then, thanks
to the rapid development of computing and information technology, the notion of “bigdata” has spread to various industries.
Apart from this initial definition, in recent years, there have been many perceptionsof big data, especially in the information industry. One of the simplest and most familiar
expressions of big data may be one that was introduced by IBM, which is one of theleading companies in the information industry [47]. It states “Data comes fromeverywhere: sensors used to gather climate information, posts to social media sites, digital
pictures and videos, purchase transaction records, and cell phone GPS signals to name afew. This data is big data” (IBM, n.d.).

12

This seems to imply two very features of big data. First, big data is regarded as a kindof huge electronic data that is explosively increasing. Although it may be a matter, ofcourse, that big data is electronic, it is important to recognize and put an emphasis on thisfeature to precisely define big data. Second, big data is being derived through sensors thathave been developed rapidly. According to IBM's definition, the term “sensor” means not
only ones such as speed meters and gyroscopes but also such as smartphones and digital
cameras, which capture information and interpret it into electronic data. Thanks to suchsensors, big data can be generated or detected.
Taking into account the definitions introduced so far, it seems possible to say that
there are three fundamental features of big data.
Electronic form

Derived through various sensors (which have been rapidly developed)The difficulty of capturing, storing, managing, and analyzing

As a general consideration of big data, three important features are identified. Big
data takes the form of electronic information, it is derived through various sensors, andthere are difficulties in capturing, storing, managing, and analyzing it. Concerning
difficulties, it is commonly considered that there are four main aspects as in Fig. 2.1,
which are volume, velocity, variety, and veracity as in [47]. These findings can besummarized as in Fig. 2.2

Fig. 2.1 Essence of 4Vs as main difficulties of treating big data, [47]13

Fig. 2.2 Overview of the important feature of big data, [47]2.1.2 Big Data in Logistic and Transportation
Big Data analytics brings many benefits to the logistics and transportation industry.
The data are collected from a very large network of sensors and devices. Big data
analytics tools are efficient in storing the data and processing them in real-time to monitor
traffic and make predictions that improve service quality and companies’ revenues [48].
Data generated by traffic sensors can be used to identify issues in real-time, whichmeans that road users can make informed decisions to save time while road authorities
may control traffic and intervene quickly when needed [49], [50]. Los Angeles, forexample, uses the data to control traffic lights, which has reduced traffic congestion byan estimated 16 percent [51]. In Dublin, the data collected from traffic sensors, bus GPS
devices, and other sources are used to build a real-time digital map of the citytransportation network. It helps identify traffic problems and make decisions regarding
the bus transportation network. As a result, the traffic flow in the city has been improved
[52].

14

The private sector may gain a competitive advantage and increase productivity using
Big Data [53]. Tracking vehicles’ locations using satellite navigation and sensors enables
customers to know exactly where their shipment is and when it will be delivered. The
vehicles’ routes can be optimized by taking into account delivery addresses and data
regarding road conditions, traffic jams, weather conditions, locations of gas stations, etc.
Routing optimization saves a lot of fuel, which reduces financial costs and environmental
impact.
Fuel consumption can also be reduced by optimizing fuel input based on data fromsensors that monitor the engine. Sensors can also monitor the state of the equipment andvehicle performance in real-time. This helps predict errors and detect when maintenanceis needed. Safety can also be improved with sensors that monitor vehicle speed, whether
the driver is complying with the traffic laws and if the driver has been behind the wheel
for too long [54].
US Xpress, an American logistics company, is an example of a company taking
advantage of Big Data [54]. It has installed almost 1,000 sensors in each truck to monitor
their locations, driving speed, petrol use, how often they break, if they are on idle for too
long, when maintenance is required and even the drivers’ capabilities. Hundreds ofbillions of data records generated help the company save over $6 million a year. It ispresented in [55] how data regarding the fuel, speed, acceleration, etc. are collected fromvehicles’ sensors and GPS devices.
They are then analyzed, which enables monitoring the driving behaviors to improve
productivity, detecting negative driving patterns, determining which trucks are idling andwasting fuel, which trucks have the worst gas mileage, and which drivers have the highest
risk factor. The data flow is shown in Fig. 2.3

15

Fig. 2.3 System data flow, [55]2.1.3 Maritime Big Data in General
Recently, a huge amount of big data is potentially available within the maritime
industries, considering the number of ships carrying vast amounts of goods to and fromthe numerous ports worldwide. For example, Big Data such as voyage data, machinery
data, weather data, business data, trouble data, AIS data, port data, ships data, route data,
and trade data are available in the maritime industries. To date, only a fraction of thisinformation is used [56].
That huge amount of Big Data is potentially important within the marine industries
and becomes a useful knowledge bank if handled carefully. Hence, big companies arelargely investing in research and harnessing Big Data [57].

162.2 The Studies of Maritime Big Data Application Area
Many studies have applied big data to the maritime industry. The followings studies
are conducted by utilizing big data.
2.2.1 Maritime Big Data and E-Navigation
With the development of navigational systems, sensors, and tracking systems
following recent advances in technology, the maritime industry is opening up to thebenefits of the digital era [25]. In this respect, the International Maritime Organization(IMO) has developed an e-Navigation concept to improve shipping through betterorganization of data on ships and onshore, and better data exchange and communication
between ships and between the ship and the shore [58]. The e-navigation Strategy
Implementation Plan (SIP) introduces a vision of e-navigation which is embedded ingeneral expectations for onboard, onshore, and communications elements. The main
objective of the present SIP is to implement the five prioritized e-navigation solutions[59]:

Improved, harmonized, and user-friendly bridge design;Means for standardized and automated reporting;

Improved reliability, resilience, and integrity of bridge equipment andnavigation information;

Integration and presentation of the available information in graphical displays
received via communication equipment; andImproved Communication of Vessel Traffic Service (VTS) Service Portfolio(not limited to VTS stations).

Consideration should be given to the IMO Formal Safety Assessment (FSA) fromwhich several required tasks have been identified. These tasks, when completed in the
period 2015–2019, should provide the industry with harmonized information, to startdesigning products and services to meet the e-navigation solutions [59]. The Maritime
17

Connectivity Platform (MCP), cloud-based technology, and an open-source digital
maritime domain enable common Internet standards to maritime navigation and transportsystems [60]. It is a communication framework enabling efficient, secure, reliable, andseamless electronic information exchange between all authorized maritime stakeholders
across available communication systems [61]. As navigation systems become moreadvanced, the amount of ship performance and navigation data thus generated is
becoming ever more significant. Big Data analytics tools make it possible for these large
quantities of data to be analyzed to gain the insight that supports the decision-making [25].
2.2.2 Maritime Big Data Innovation in the field of Weather Routing
Weather conditions may cause several issues in maritime transport. Due to thedirection or strength of winds and waves, the engine power needs to be increased tomaintain the speed. By BDA of historical weather data, eventual harsher weather
conditions on certain routes can be detected and data could be used to adapt the future
strategic route planning [62]. Furthermore, data about the ship's performance andnavigation information, such as the location, average draught, main engine power, speed,and fuel consumption combined with wind speed and direction, can support navigation
strategies, as well as intelligent decisions, in real-time as well [63]. With the provision ofreal-time weather data (wind speed and direction, wave heights, storm forecasts) and shipdata (regarding the trim and rolling), dangerous situations can be identified immediately.
Furthermore, alternative (less hazardous) routes outside critical areas can be calculated
faster. The data regarding the ships that are already within a hazardous area can help otherships to avoid the same difficulties, thus improving the safety of the crew and other ships.
The risk of sinking or cargo loss due to severe weather conditions may be minimized.
For example, the port of Singapore uses a platform that enables information on shippositions and weather data to be collected. The platform helps avoid accidents byinferring the most likely path ships would take in a given situation [37].

18FastSeas [64], as another example, is a cloud-based weather routing and passage
planning tool that enables calculating the fastest route (from point A to point B) according
to the current NOAA GFS weather forecast (a weather forecast model produced by the
National Centers for Environmental Prediction), current oceanic currents, vessel
performance, and “comfort criteria”. FastSeas has to be “taught” the vessel performance.
The performance settings must be set only once. FastSeas will save these settings and use
them next time. After the calculation is complete, the route will be displayed on the map.
To achieve an efficient maritime transport service, shipping companies can also
implement specialized software for performance and route analyses [65]. Weatherrouting software can help users determine the optimal departure time and routing basedon user-specified parameters. These parameters can include maximum acceptable windspeeds or wave heights, as well as the percentage of time that the vessel will be on aparticular point of sail [66].
Weather-routing can also enable fuel savings, which will not only bring the benefit
such as the cost reduction but will also support the achievement of emission standards
and support the Ship Energy Efficiency Management Plan (SEEMP) [25], [63]. Thereexist several examples of such software such as the Transas Navi-Planner, Octopus, andBonVoyage. Transas Navi-Planner utilizes one of the world’s largest navigational
databases, as well as artificial intelligence to auto-create a route that is safe to sail. It
calculates weather optimization, supports hazard identification, creates a voyage plan,and provides at all times the latest charts and data automatically [67]. The OCTOPUS
software assists ship officers and engineers in making real-time decisions enabling them
to be proactive in safety and efficiency actions, resulting in more immediate benefits thanjust a traditional post voyage analysis [68].
The BonVoyage System is a combination of software and data. BonVoyage is a userfriendly icon-driven graphical marine weather briefing system. By using real-time
weather data to generate color-coded graphics, the software lets the user see into the heart19

of severe weather systems. This provides a much more complete picture of the storm than
traditional text-based weather bulletins or radio facsimiles because it captures the detailed
shape of each storm system and visually displays dangerous wave generating areas [69].
2.2.3 Maritime Big Data Innovation in the field of Monitoring/Tracking
To avoid collisions of ships in seaport areas, forecasts of vessel arrival times withreal-time information about their current speed are made to illustrate the estimated traffic[58]. Through this technology, collision issues are identified and vessels are notified
before accidents could happen. The encounter of two vessels in narrow areas can be
avoided. For example, the Vessel Traffic Service is designed to improve maritime safety,
support security activities in the maritime sector, promote smooth and efficient maritime
traffic and prevent accidents and the potential environmental harm they may cause. The
system displays a graphical environment with movements of vessels in the approach areas,
putting each of these overlapping vessels to a digital nautical chart, in its real geodesic
position and informs the identification of each vessel [70].
Risky shore areas (such as bunkering facilities for fuel or liquid cargo) require a
special control in order, for example, to detect unauthorized ship movements as early as
possible by monitoring the information about all vessel and vehicle movements [71].
Collisions outside the seaport areas can be avoided by collecting data about the standard
routes of vessels on a long-term basis. These data reveal if a vessel deviates from thedeclared path [25].
Monitoring of technical equipment enables higher safety for the crew andenvironment and increased the longevity of ship components. During the use of theequipment and engines, data are collected and sent to manufacturers, who can detect
irregularities and increase their service quality [24]. The Satellite AIS (S-AIS) is a vessel
identification system that tracks the location of vessels in the most remote areas of theworld, especially over open oceans and beyond the reach of terrestrial-only AIS systems.
S-AIS is used for collision avoidance, identification, and location information as well as
20

for maritime domain awareness, search and rescue, environmental monitoring, andmaritime intelligence applications [72]. Analyses of oceangoing ship routes are also
important in the function of environmental protection. The analyses of ship routes in thementioned oceanic areas covered by S-AIS could be very important to estimate air
pollution emissions from ships, seeking to minimize their negative impact [73].
2.2.4 Maritime Big Data Application in Ports and Terminals
Similarly, to previous analyses, Big Data Analysis (BDA) can also be used forpredictions of arrival times and calculations of the required speed. In general, shipping
companies are under the pressure of meeting the agreed arrival times at the seaport, while
being faced with the problem of the named uncertainties of weather conditions.
Consequently, managers often decide to deviate from the efficient low steaming toprevent delays [62]. On the other hand, seaport operations are sometimes delayed due todifferent reasons, such as arrival times of prior ships, loading or unloading processes, etc.
Both circumstances will lead to waiting times on available slots for the arriving ship,
being anchored outside of the port.
To prevent this situation, the performance and weather data can be analyzed andconsidered with tracking data, as provided by the AIS. The planned (optimum) routematched with real-time data on outside circumstances, like the weather, and the tracked
position can thus be used to forecast arrival times [25]. However, it is necessary to divide
the standard AIS transceiver from AIS Application Specific Messages (referred here). An
AIS transceiver transmits and receives static data (Maritime Mobile Service Identity, ship
name, and callsign), as well as dynamic data (position data, speed, and course overground) via VHF on two channels specially reserved for AIS [74]. AIS Application
Specific Messages (ASMs) are a quick way to get important information through the VDL
in small, predefined packages. A ship, for example, can use the ASMs to transmit
important details such as dangerous cargo information, number of persons on board,
extended ship static and voyage-related data, and route information [75].
Data Science Campus (DSC) [76] has explored the likelihood that a ship would bedelayed and would arrive at its intended destination sometime after its estimated time ofarrival. Ship delays were predicted using supervised machine learning and these21

predictions can be used to predict port loading at a point in time and can support any
subsequent operational port planning. Both the Automatic Identification System (AIS)
and the Consolidated European Reporting System (CERS) datasets have been used and ameans by which they could be joined has been developed. The common identifier
between the AIS and CERS data is the Maritime Mobile Service Identity (MMSI, theunique identifier of each ship) [76]. To merge the two datasets they were first inner joinedon the MMSI, the datasets were then filtered by restricting each timestamp within the AIS
to its closest estimated time of arrival (ETA) or estimated time of departure (ETD) (both
ETA and ETD were considered since the AIS data contained both the inbound andoutbound portions of the journey). Records were retained where the ETA or ETD werefalling within 24 hours of the timestamp. As more than one ETA or ETD can meet thesecriteria, the ETA or ETD closest to the timestamp was selected. After the merging, thecomplete dataset included 727 voyages relating to 235 unique ships. An in-depth
explanation of the research can be found in the report [76].
Big Data enables optimized usage of resources and infrastructures as well. For
example, a typical crane operator works only one-quarter of the time, remaining idle forthree-quarters of the time, waiting to get a container ready to load or for an empty truck
to load a container. Increasing the number of trucks may not be a viable solution owing
to the congestion it would cause. Rather, Big Data analysis could synchronize movements,
so that the crane operator can work more time. For instance, signals related to the craneposition, status, and GPS position signals could sync the movement of trucks andcontainers, to reduce idling time [77].
The Bahri maritime company has rolled out a series of initiatives aimed at harnessing
the power of “Data Innovation” to enhance productivity, unlock growth opportunities,
and transform the existing operations model in the shipping industry [78]. In this respect,
the company has created BahriData, a Big Data platform, to improve the operating
performance and to unlock growth opportunities. The company has developed various
data models in its BahriData platform to cover various key business areas such as
chartering, voyage management, fleet operations, maintenance, and customer services
[37].

22

The following are examples of big data technology implementation in ports:
Ports in Singapore and Malaysia
In Singapore and Malaysia, ports utilize big data techniques to create advanced
inspection systems to assess the history and cargo type of importers.
PurposeThe purpose is to segregate importers that most require inspection and allow otherimporters to operate quickly, without impacting the port’s security objectives.



Maritime and Port Authority, Singapore
In August 2015, Singapore’s Maritime and Port Authority (MPA) signed a
Memorandum of Understanding (MOU) with IBM to establish a two-year agreement
for big data. The agreement involved developing a platform using IBM’s Traffic
Prediction tool to forecast vessel arrival times and estimate potential traffic
congestion using fusion analytics. The platform also relies on data mining andanomaly detection by using IBM Incident Detection Module and IBM System G.
According to Goh Kwong Heng, CIO of MPA, the port authority plans to invest in
big data to improve port operations and activities. The MPA aims to use data
analytics platforms to complement its port management systems in detecting
anomalies and supporting both operations and planning processes. The authority alsoplans to invest in technologies such as drones and mobile apps to manage marine
accidents and improve the efficiency of its port workers.
PurposeThe port uses the platform to improve productivity and marine safety at the majortrans-shipment hub. For example, by collecting information on ship positions andweather data, the platform helps avoid accidents by inferring the most likely pathships would take in a given situation. This also helps prevent illegal bunkering bydetecting unusual movement patterns. According to Andrew Tan, the CEO of MPA,
23

the port authority’s collaboration with IBM enabled a mix of research expertise,software technologies, and maritime domain knowledge to create new capabilities
for the maritime industry in Singapore.
Hamburg Port, Germany
The Hamburg Port in Germany uses a cloud-based analytics tool called SmartPort
Logistics. The tool pulls different types of data, such as vessel positions, height, andwidth of bridges, and planned driving routes. This data can be viewed in real-time on
mobile applications.
Purpose
The tool aims to streamline the flow of goods. It allows port workers to know
precisely when ships are expected to dock, and truck drivers to know when cargo isexpected to be offloaded.

Port of Cartagena, Columbia
The port uses Cisco and IBM solutions for IoT analytics. The solutions help forecast
equipment failures and keep our machines running effectively by ensuring theequipment degradation with needed maintenance.
PurposeThe solutions ensure proper and timely maintenance of port machinery equipment
and ensure the fleet runs even more efficiently, keeping vessels and cargo moving

smoothly in and out of the port.
Port of Rotterdam, The Netherlands
The Port of Rotterdam has recently implemented big data analytics. CargoSmart
Limited, a global shipment management software provider, recently announced that
the Port of Rotterdam Authority is using its sailing schedule data to provide shippers

24with greater visibility to the available route options through its gateway. The Port ofRotterdam launched Navigate on May 10, 2017.
The online tool incorporates the deep sea and short sea schedules of 550 ports
worldwide as well as the rail and barge connections between Rotterdam and over 150
European inland terminals. Navigate also contains a business directory with over
1,500 companies that are active in and around the Port of Rotterdam, as well as an
empty depot tool. Users can find and compare the best connections throughRotterdam based on modes and expected transit times to make more informed
decisions.
Incorporating CargoSmart’s ocean schedules with the port’s intermodal schedules
allows the Port of Rotterdam to may provide a complete picture of its logistics
coverage for shippers. CargoSmart may also save the port time from obtaining andcontinuously updating the schedule data from ocean carriers.
PurposeSustainable supply chain analysis for Reefer containers.
2.2.5 Studies of Maritime Big Data for Safety Improvement
The security of ship operations can be affected by several different reasons. To
improve the general security of ship operations and to eliminate causes such as acts ofpiracy, various data about ship details and positions need to be monitored. The use of Big
Data can not only prevent accidents caused by weather conditions, but also those causedby other participants in maritime traffic. In some seas, acts of piracy still pose a great risk
to shipping. Information regarding the most recent pirate activities can help to divert otherships that are currently on the same route. One of the systems in the field of security is
the Long Range Tracking and Identification (LRIT). It is an international tracking andidentification system incorporated by the IMO under the SOLAS convention to ensure a25thorough tracking system for ships across the world [79]. The system aims to enhance
security for government authorities. LRIT provides ship identity and current location
information insufficient time for a government to evaluate the security risk posed by aship off its coast and to respond to reduce the risk if necessary. The system complements
existing modes of tracking ships using coast-based AIS stations. Furthermore, thecombined use of LRIT and Satellite-AIS data can increase the tracking quality andcoverage of ships registered in the EU CDC [80].
Safety at sea can also benefit from implementing anomaly detection in marine
operations. An overview of several machine learning techniques that can be used to detect
anomalies from data gathered on vessel movement is presented in [81]. In [82], anapplication of sensor-based anomaly detection in maritime transport is demonstrated.
Sensor data, which include environmental and ship system information, are streamed
from a ship to shore where they are analyzed through a Big Data analytics platform. Auto
Associative Kernel Regression and the Sequential Probability Ratio Test technique areused to detect anomalies and trigger alarms so that appropriate action can be taken assoon as possible. Critical points identified along a vessel trajectory are shown in Fig. 2.4.

Fig. 2.4 Critical points identified along a vessel trajectory, [83]26A solution for real-time monitoring of sensor data in a seaport infrastructure
implemented in the Puerto de La Luz seaport in the Canary Islands is described in [84].
The system integrates data from AIS, various sensors, and external sources, and provides
a 3D map showing the ingoing and outgoing vessels, as well as the environmental
conditions. It is equipped with an alert system which means that the port can easily
identify issues and notify the vessels to prevent collisions, help the vessels avoid highwaves, etc. Another example of Big Data used for anomaly detection is CMAXS, a
maintenance system developed by ClassNK [85]. It uses real-time data collected fromflow, pressure, and temperature sensors on all engines and pumps.
The data are analyzed for anomalies to detect potential damage as well as generate
recommendations that help minimize downtime and reduce repairs. In [86], thearrangement of Precaution Areas, whose purpose is minimizing the possibility ofcollisions, is optimized using AIS data. On the other hand, [87] shows how Big Data canbe used to gain a better understanding of maritime activities, which is especially usefulin remote areas such as the Arctic where shipping activity needs to be monitored to ensuresustainability and the information is otherwise difficult to access. It also discusses
anomaly detection, such as detecting when a vessel deviates from the declared path,falsifies its AIS reports, or turns off its AIS transponder to potentially engage in illegal
activities.
2.2.6 Maritime Big Data Application by NYK for Operational Efficiency,
Safety, Maintenance.
In shipping, potential technical revolution areas are shifting from the Eco Ship to
Smart Ship and from the hardware to software field. A key element of the revolution inInformation Communication Technology (ICT). Throughout the maritime industries,
researchers and developments are taking place that analyzes and utilizes any types of datataken from the ship to improve operational safety and efficiency. ICT toolkits include a27

variety of fields such as IoT, satellite communications, data analysis, Apps, system
integrations, automation, and robotics.
In the industrial machinery fields that have taken the lead in this, for example in thewind turbine industry, the products are being developed with sensors and intelligent
systems. It can prevent unexpected downtime, reduce maintenance costs and improveenergy efficiency through their life cycle with condition monitoring, big data analysis,
and supports from service engineers. IoT data collected from the real wind turbine are fed
into virtual wind turbines on computers. The actual conditions of each wind turbine canbe reproduced on computers using IoT data and data analysis and simulation technologies.
It represents the current status of each wind turbine and allows forecasting under various
estimated scenarios.
The digitalization activities related to harnessing maritime Big Data from NYK(Nippon Yusen Kabushiki Kaisha) is shown as follows:

Implementing Ship Information Management System [SIMS]
Through the implementation of SIMS (Ship Information Management System)in 2008, the NYK Group has been able to share data among workplaces on land andsea in real-time, including detailed hourly updates on shipping operations and data
related to fuel consumption. Optimized economic vessel operations and energysaving operations are realized by visualization of information and close informationsharing among crew members, shipowners, ship operators, and ship managers. SIMS
has been installed on 190 of the operating vessels of NYK (as of the end of March2019).



Making Onboard Work More Efficient [UMS Check System]
An unmanned machinery space (UMS) check system enables the operation statusof onboard machinery to be checked and recorded when conducting the unmanned
28

operation of an engine room at night. We introduced digital tablet entry for theapproximately 2,000 items that were previously checked on paper, and this reduced
crew work for identifying anomalies and allowed us to easily share data with othervessels. NYK also promoting the development of a system to enable detected
anomalies to be notified to shoreside personnel and thereby identify machinerytrouble at an early stage.
Optimizing navigation plans, making route planning more efficient, andenabling swift support of emergencies [J-Marine NeCST]
NYK, MTI, and Japan Radio Co. Ltd. recently teamed up to develop J-Marine
NeCST, an operational support tool that lets users manage and share electronic chartsand other voyage information on large-screen displays. By fusing its years ofexperience in vessel operation management with its partners' technologicalcapabilities, the Group has successfully developed a one-of-a-kind tool that not only
lets users write information by hand on electronic charts but also boasts incredible
operability and maneuverability. The tool even has a feature that integrates preparedroute information with the Electronic Chart Display and Information Systems(ECDIS) that all large cargo vessels making international voyages must now have.
The tool also lets users superimpose meteorological and sea conditions information,
thereby streamlining and optimizing the process of drafting voyage plans. By
digitizing vessel-specific information, as well, the tool enables information-sharing
among ships and land-based sites to make onboard and onshore work processes more
efficient.

Launching Shipmanagement Platform [NiBiKi]
The NYK Group developed the ship management platform for data sharing
called "NiBiKi" and launched services in December 2018. By the Safety
Management System (SMS) manual, the onboard crew is required to file reports
about safety management to the ship-management company. However, the29

conventional operation flow had some inefficiencies. Crews would have to draft
various reports and applications, and then send emails with attachments to the ship
management company to obtain approval. Afterward, crews would need to file
printouts of the reports to retain on board. Moreover, the information reported by theship was not being used efficiently to analyze this data, etc., because each vessel andship management company would file the data independently.
Given this issue, the NYK Group developed the NiBiKi system, which
digitalizes SMS manual documents and the application and approval workflow,
thereby making it possible for crews to report and request approval accurately in ashort period of time by simply completing the prescribed forms following the
instructions. Furthermore, the data accumulated in the NiBiKi system will be sharedwith ship operators and ship management companies for high-quality big data
analyses, enabling linkages with conventional safety and crew health activities.
Going forward, we plan to build a more comprehensive system and further utilize
data for crew training and drills.
Supporting Safe Vessel Handling [i.Master]
Docking and undocking are some of the tensest times in vessel operations. To
assist in reducing the risk of colliding with the quay, the Group has introduced
i.Master software for handheld digital charts. The software gives crew members abird's eye view of the vessel's movement and the surrounding situation. Via a tablet
computer, the software shows crew members the course of a vessel and its docking
or undocking speed and automatically identifies other vessels. Furthermore, thesystem allows crew members to monitor docking and undocking constantly without
being on the bridge.
Collisions with the quay could force vessels to lay up for long periods. As wellas inconveniencing customers, such delays would lead to a loss of trust. By usingi.Master effectively, it can reduce the risk inherent in docking and undocking.
30

i.Master is just one example of the innovative efforts to develop useful technologies
for a range of operational situations and thereby build systems that ensure stable,safe, and efficient vessel operations.


Taking Part in "i-Shipping," a Japanese Government R&D Project for
Advanced Safety Technology
IoT and other information technology are showing enormous potential totransform the shipping industry. Japan's Ministry of Land, Infrastructure, Transport,
and Tourism (MLIT) is promoting research and development projects in this newtechnology area for a "productivity revolution in the maritime business" (i-Shipping).
NYK, MTI, and other industry partners are now cooperating and pursuing
technological development initiatives on four i-Shipping projects, including "vesselmachinery plant trouble-prevention" and "collision avoidance and autonomous
operation."
Other researchers also harnessing maritime big data for energy efficiency

improvement. Perrera et al. [88] propose the data flow chart as presented in Fig. 2.5. The
data are collected from various onboard sensors and data acquisition systems,
preprocessed and transmitted to shore-based data centers where they are stored andanalyzed. The result of the analysis is information that supports decision-making, forexample, to improve energy efficiency and system reliability.
Monitoring fuel consumption, various emissions, the use of lighting, heating, andsimilar processes can result in insights that support decision-making. In [53], data suchas wind speed and direction, average draft, trim, main engine power, shaft speed, and fuelconsumption are analyzed and several higher fuel consumption trends under theseparameter variations are noted. The optimal trim configuration is identified concerning
the fuel consumption rates. Applying strategies based on this information enables ships

31

to meet energy efficiency and emission control standards. Along with environmental
benefits, this is also significant for cost reduction.

Fig. 2.5 Data flow chart in ship performance and navigation information, [53]2.2.7 Maritime Big Data Application for Other Purposes
The following table provides a snapshot of the application and the key plan
application areas for big data in the maritime industry [89].
Table 2.1. Application area for big data in the maritime industry
Role

Function

Example of Big Data Application
Energy-saving operation

Operator

Save operation
Schedule management

Ship Operator

Fleet allocation
Fleet Planning

Service planning
Chartering
Safe operation
Condition monitoring and maintenanceShipownerTechnical management

Environmental regulation compliance
Hull and propeller cleaning
Retrofit and modification

New building

Design optimization
32



Chartering
A key function of charterers is to find the right ship for cargo at the most economical
price. The task is highly dependent on information provided to them by known
brokers and ship owners. However, as this information is limited, it may or may not
be most efficient. Big data analytics can provide charters with readily available,
accurate, and actionable information to improve decision-making.
Charters can integrate Automatic Identification System (AIS) information, position
reports, estimated times of arrival, vessel particulars (such as size), and market
information into an exchange portal to find all available alternatives as well as thefreight forecast. This can give charterers and ship owners access to more options thus
improving transparency and competitiveness. Bahri, the national shipping company
of Saudi Arabia, has developed various data models in its new data platform,
BahriData, to cover various key business areas such as chartering, voyage
management, fleet operations, maintenance, and customer services.



Operations
Speed: Ships, like automobiles, have optimum speeds, and various tests are
conducted at the time of vessel delivery to determine the optimum speed for fuelconsumption. However, operating a vessel at its optimum speed is difficult as itchanges over time due to a variety of factors such as engine wear and maintenance.
Big data analytics can help shipowners determine the optimum speed for fuelconsumption, taking into consideration factors such as bunker cost, freight rates, andschedules.
Maintenance: Decisions regarding vessel maintenance, including hull cleaning andpropeller polishing, are taken based on intuition or a schedule rather than on actual
vessel performance. Fuel consumption data can also be used for cost-benefit analysis

33

of vessel maintenance. Data analytics can make it easier for operators to decide thetiming and the benefits of performing maintenance.
Voyage Operations
Terminal operators, voyage managers, or port agents need estimated time of arrival(ETA) and cargo information. Vessels can be tracked using dashboards instead of
relying on notes, emails, or phone calls. This helps in making more effective
decisions about terminal and berth allocation, cargo handling, and route tracking.
Dashboards can also provide information about any deviations from optimum
performance. The ideal route, the weather service-provided route, and the actualroute can be tracked in real-time. Any changes to speed, ETA, and other factors canbe tracked and managed in real-time, thus ensuring that the voyage goes as planned
and remains profitable.
* ClassNK-NAPA GREEN offers a real-time big data analysis performancemonitoring and optimization solution. The solution passes data collected fromboth on-board and shore-side sources through advanced and predictive algorithms
to deliver information on current operations and on potential operational changes
to allow vessels to reduce fuel consumption.

Vetting
Vessel owners and operators try to ensure that their fleets are acceptable for use bycharterers. Instead of improving the vessel quality, they focus on meeting or passing
the acceptance criteria. The process of vetting includes getting feedback from various
entities such as inspectors, terminals, and port state authorities, as well as operator
self-assessment. Data analytics can help charterers and vetting organizations analyze
the different sources of information and select the right vessel with the least amountof risk involved in pollution preparedness, safety management, and navigation

34

2.2.8 Maritime Big Data in this Studies
Recently, the era of shipping records big data has been started along with mandatory
digitalized ship movement with AIS (Automatic Identification System). Substantial dataamount is generated by AIS, such as the ship's unique identification of international
maritime organization number (IMO number), position, course, speed, and destination[89]. AIS is required to be installed for the ship on international voyages of over 300 gross
tonnage and all passenger ships.
An AIS transmitter exchanges data with other nearby ships, AIS land-based systems,
and satellites with the purpose of collision avoidance [89]. Moreover, maritime logistics
big data, such as ship and port specification data, route data, international trade data, anddata provided by AIS, are currently available and can be used. Based on that, some studies
have been conducted on big data utilization to improve ship construction, ship operations,and ship maintenance [90-92]. However, studies regarding the utilization of such
maritime logistics big data for ship basic planning or ship design are limited and havebeen identified as important future tasks [89].
Big data use in this study is consists of AIS data, ship operational data, ship data, port
data, route data, and trade data.

Fig. 2.6 Big data in this study35

2.3 Characteristics of this Study
The characteristics of this study are represented based on the following comparison
with the previous studies as follows:
Study object

Main analysis

Data Sources

Literature

E-Navigation

Ship navigation, ship

VTS, AIS, Sensors &[25][58][59][60][61]communication

Tracking System

Weather
Routing

Route decision,planning, collision
avoidance

Data Acquisition
Module, AIS[25][37][62][63][64][65][66][67][68][69]Ports &
Terminals

Ship congestion,
delays, safety andmaintenance

AIS, ETA & ETD[25][62][74][75][76][77]

SafetyImprovement

Ship security andsafety at sea

Long Range Tracking
and Identification(LRIT)[79][80][81][82][83][84][85][86][87]Operational
Efficiency

Ship management,
FOC monitoring

J-Marine NeCSTNiBiKi, i-Shipping[53][63][88][90][92]DemandForecasting

System dynamic,
demand forecastingStatistical Data, Big
Data[14][17][39]MarineLogistics

Ship basic planning
Cargo assessment
Ship allocation

MLDB(Marine Logistics
Database)

This Study

2.3.1 Marine Logistic Databases (MLDB)In this study, the authors developed the MLDB using AIS and statistical data. The
MLDB consists of the latest marine logistics data, i.e., operation information from AIS,
ship, port, route, and international trading information, as shown in Fig. 2.7. The data aremanaged, integrated, and structured to derive valuable insights from information buried
in marine logistics data.

36

Fig. 2.7 MLDB
2.3.2 Ship Allocation Model
The ship allocation model constructed aims to replicate the actual ship allocation.
The actual shipping market has the essential affection in the actual ship allocated. Byutilizing the MLDB, the scheme of ship allocation model used in this study is shown in
Fig. 2.8. With the ship allocation model scheme, the ship allocated in chartered contract
scenarios, and long-term contracts are to be concluded on an annual cycle where a single
shipowner manages certain freight requests continuously in one year.
The ship allocation model consists of three specified models, shipper model,
shipowner model, and operator model. The shipper model creates the clusters of exporterand importer ports, and its cargo demand is to be transported in the selected year. Later,
the demand estimated will be used in the operator model as a freight transport request to
the shipowner model. Following the request, the shipowner bid the cost and transportable
volume on the route where the shipowner ship operates. To finalize the charter contract
between operator and shipowner, the operator will choose the served bid in economical
consideration and create ship allocation.

37

SHIP ALLOCATION MODEL
Shipper Model

Operator Model

Shipowner Model

Transportation
DemandEstimation of
DemandEstimation
ResultShip
Allocation

Fig. 2.8 Ship allocation model
2.3.3 Importance of Bulk Carrier
2.3.3.1 World Seaborn Trade
Seaborne trade was measured in ton-miles to reflect distances traveled and theemployment of ship capacity increased by 5 percent in 2017, up from 3.41 percent in2016. Overall ton-miles generated by seaborne trade in 2017 amounted to an estimated
58,098 billion tons (Fig. 2.9). Much of the growth was driven by crude oil and coal
shipments, which have greatly benefited the shipping industry, given the growth involumes and distances. Crude oil trade contributed 17.5 percent to ton-mile growth while
major dry bulks contributed nearly one-third. Together, minor bulks and other dry cargoaccounted for 17.7 percent of ton-mile growth, while containerized shipments contributed
17.4 percent. The contributions of gas and petroleum products were much smaller [93].

38Fig. 2.9 World seaborne trade in cargo ton-miles, 2000–2018 (BTM), [93]2.3.3.2 Dry-cargo trades: The mainstay of seaborne trade in 2017
Following a limited expansion in 2015–2016, global dry bulk trade1 grew by about
4 percent in 2017, bringing total volumes to 5.1 billion tons (Table 2). A sharp increasein iron ore imports to China, a rebound in the global coal trade, and improved growth inminor bulk trades supported the expansion. Overall, strong import demand in China
remained the main factor behind growth in global dry bulk trade. An overview of global
players in the dry bulk commodities trade sector is presented in Table 3-6 [93].
Iron ore imports to China increased by 5 percent in 2017, bringing total volumes tonearly 1.1 billion tons. With a market share of more than 70 percent, China remains themain source of global iron ore demand. A rise in steel production and the closure of morethan 100 million tons per annum of outdated steelmaking capacity in 2016–2017 boosted
the country’s demand for imports. Further, the increased use of higher-grade imported
39

iron ore displaced domestic supplies. The leading iron ore exporters were Australia,
Brazil, and South Africa; Australia and Brazil supplied over 85 percent of the demand forimports in China. Nevertheless, Australia is by far the largest exporter, supplying nearly
two-thirds of iron ore requirements in China. The country imports 21 percent of its ironore requirements from Brazil, which benefits the dry bulk shipping industry through long
distances. South Africa generates 4 percent of all iron ore imports to China. Other
suppliers, such as India, the Islamic Republic of Iran, and Sierra Leone, have also
increased their exports to China.
Table 2.2 Dry bulk trade 2016–2017 (MT and % annual change)
Dry Bulk

2016

2017

Percentage change 2016-2017

Main Bulks of which

3040.93196.3

5.1

Iron ore

1418.11472.73.9

Coal1141.91208.5

5.8

Grain

480.9

515.1

7.1

Minor Bulks of which

1874.6

1916.5

2.2

Steel products

406.0

390.0

-3.9

Forest Products

354.6

363.6

2.5

Total Dry Bulks

4915.55112.84.0

Global grain trade, including wheat, coarse grains, and soybeans, reached515.1 million tons in 2017, a 7.1 percent increase over 2016. Exports are dominated by afew countries, notably the United States; importers tend to be regionally diverse. As inother dry bulk trades, Asia was a driving force of growth, albeit not the only one. In 2017,
grain trade was underpinned by a 14.7 percent increase in soybean imports to China andgrowing exports from Brazil and the United States.

40

Table 2.3. Major dry bulks and steel: Producers and Users 2017
Steel Producers%

Steel Users

%

China
Japan
India
United States
Russian Federation

49
6
6
5
4

China
United States
India
Japan
Republic of Korea

46
6
5
4
4

Republic of Korea
Germany
TurkeyBrazilOthers4
32
219Germany
Russian Federation
TurkeyMexicoOthers332225

Table 2.4. Major dry bulks and Iron Ore: Exporters and Importers 2017
Iron Ore Exporters%

Iron Ore Importers%

Australia
BrazilSouth Africa
CanadaIndia
Others

56
26
4
3
2
9

China
Japan
EuropeRepublic of Korea
Others
-

72
9
8
5
6
-

Table 2.5. Major dry bulks and Coal: Exporters and Importers 2017
Coal Exporters%

Coal Importers%

Indonesia
Australia
Colombia
United States
South Africa
CanadaOthers
-

32
30
7
7
7
2
15
-

China
India
Japan
European Union
Republic of Korea
Taiwan Province of China
Malaysia
Others

18
17
15
13
12
6
3
16

41

Table 2.6. Major dry bulks and Grain: Exporters and Importers 2017
Grain Exporters%

Grain Importers%

United States
Russian Federation
UkraineArgentina
European Union

25
23
15
11
9

East and South Asia
AfricaDeveloping America
Western Asia
European Union

34
21
20
16
7

Australia
Canada
Other

8
7
2

Transition Economies-

2-

China dominates the soybean trade and accounted for nearly two-thirds of the global
soybean import demand in 2017. Outside Asia and the European Union, some lesser
consuming regions, such as Africa and Western Asia, also contributed to such growth.
Tariffs by the United States on certain goods imported from China, including steel andaluminum, and retaliation by China, may lead to restricting soybean import from theUnited States.
China is the world’s largest consumer and importer of uncrushed soybeans. However,it may decide to replace imports from the United States and source its soybean
requirements from alternative suppliers such as Brazil. While trade restrictions generally
portend ominous consequences for shipping, a shift in suppliers and routes in this context
may have an unintended positive effect on ton-miles generated.
Growing manufacturing activity and construction demand supported a 2.2 percent
increase in minor bulks commodity trade. Rising demand for commodities such as bauxite,
scrap, and nickel ore pushed volumes to 1.9 billion tons. However, the large drop (less30.8 percent) in exports of steel products from China due to reforms in the country’s steel
sector undermined the expansion to some extent. Bauxite shipments expanded by
19.5 percent, accounting for 13 percent of minor dry bulks commodities trade in 2017.

42

The continued rise in Chinese aluminum production and the availability of bauxite
ore, following years of export disruptions, led to an expansion in the bauxite trade. While
China dominates the import side with a market share of more than two-thirds, key players
on the supply side are more varied and include Australia, Brazil, Guinea, and India.
Nickel ore trade rose by 7.6 percent, highlighted in particular by increased growth innickel ore shipments from Indonesia, following its decision to relax its export ban on
unprocessed ores.
Based on the information above, the global bulk seaborne trade is expected to be
growing higher in the next years. Based on this condition, Bulk Carrier transportation isimportant. However, the research of ship basic planning support system for Bulk Carrier
has not been developed. Based on the information appointed above, this study is focusedon the decision of ship specifications considering ship operations for the latest market
conditions. To realize that, the system which can generate ship allocation that matches areal ship allocation is developed based on the data extracted from MLDB. Therefore,
thorough executing the simulation by changing the ship specifications or future scenarios,
such as fuel price and cargo demand between ports, the competitive ship principal
particulars and performance can be examined.

43

Chapter 3
Basic Concept
3.1 Marine Logistic Database (MLDB)Marine Logistic Database denoted as MLDB is defined as a collection of Marine Logistic
Big Data sets that are integrated into a relational database. MLDB is developed byintegrating maritime logistics big data, such as AIS data, ship operation data, ship andport data, route data, and trade data. The basic concept of MLDB is described below.
3.1.1 MLDB Input Data
To develop the MLDB, we employed the following data as input.


AIS data: indicated speed, indicated draft, ship position, timing arrival, and departuredates, and arrival and departure port collected from the Market Intelligence NetworkPort data: port name, longitude, latitude, port dimension, and cargo handlingcollected from Sea-web Port

Ship data: ship name, deadweight, International Maritime Organization number,classification, ship dimension, operator, shipbuilder, ship status, and build year
collected from Sea-web Ship

Route data: departure port, arrival port, route choices, and distances collected from
Sea-web Port and IHS-FairplayTrade data: commodity trade, the period of trade between countries, commodity code,
trade value, trade quantity, reporter, and partner collected from UN Comtrade.

443.1.2 MLDB Structure
To more easily extract valuable information, the structure for the MLDB should be
defined and modified unstructured data into a relational database. For example, byintegrating ship and port data with operation data, some information related to a ship’s
operational state can be analyzed (e.g., berthing, anchoring, or sailing).
3.1.3 Error Cleaning
Massive amounts of data are available for the organization which will influence theirbusiness decision. Data collected from the various resources are dirty and this will affectthe accuracy of prediction results. Data cleansing offers a better data quality which will
be a great help for the organization to make sure their data is ready for the analyzing
phase. However, the amount of data collected by the organizations has been increasing
every year, which is making most of the existing methods no longer suitable for big data.
The data cleansing process mainly consists of identifying the errors, detecting the errors,
and corrects them. Despite the data need to be analyzed quickly, the data cleansing
process is complex and time-consuming to make sure the cleansed data have a better
quality of data [94].
Incomplete information will generate uncertainties during data analysis and this must
be managed in the data cleansing stage. Errors or missing values in the dataset willproduce a different result and may affect the business decision. The data must be accurate
to avoid losses, problems, and additional costs due to the poor quality of data. For
example, according to the “Price Waterhouse Coopers” survey conducted in 2001, 75%
of 599 companies have suffered losses due to data quality issues [95]. Since thesebusinesses rely on data like customer relationship management and supply chain
management, therefore they need to have excellent quality data to achieve a more precise
and useful result. Quality data only can be produced by data cleansing as the datacollected from the various sources might be dirty [96].

45Data quality can be defined as the fitness of data to fulfill the business requirement.
It is achieved through people, technology, and processes. It ensures compliance andconsistency particularly when data from different databases are combined. Without
proper data quality management, even a minor error might cause revenue loss, process
inefficiency, and failure to comply with the industry and government regulations [97].
Thus, data quality and data cleansing are always linked together as ensuring data quality
is critical and necessary before the sharpening of analytic focus can occur [98].
Investigations into the problems related to data quality can be traced back to as earlyas the late 1960s when a mathematical theory for considering the duplicate problem instatistical data sets was proposed by Fellegi and Sunter [99]. However, it is only in the
1990s that the data quality problem has been considered in computer science with the datastored in databases and data warehouse systems. More and more people have becomeaware that poor data quality is one of the main reasons for the failure of a database project.
Though a variety of definitions for data quality have been given [100-102], studies showthat still, no formal definition for data quality exists [101].
From the literature, data quality can be defined as “fitness for use”, i.e., the ability ofdata to meet the user's requirement. The nature of this definition directly implies that theconcept of data quality is relative. Orr states “the problem of data quality is fundamentallyintertwined in how our system fits into the real world; in other words, with how users use
the data in the system” [101]. This has two interpretations: one is that if a data set isavailable and is as good as it can be, there are no other options than to use it. The otherone is that what is considered quality data in one case may not be sufficient in other cases.
For example, an analysis of the financial position of a company may require data inunits of thousands of pounds while an auditor requires precision to the pence, i.e. in real
life, it is the business policy or business rules that determine whether or not the data is ofquality. Generally speaking, data quality can be measured or assessed with a set ofcharacteristics or quality properties called data quality dimensions [103]. Some46

commonly used data quality dimensions include accuracy, completeness, timeliness, andconsistency, which can be defined as:Accuracy – conformity of the recorded value with the actual value;Timeliness – the recorded value is not out of date;Completeness – all values for a certain variable are recorded;Consistency – the representation of data is uniform in all cases.
Therefore, data quality can be considered a multi-dimensional concept. These data-

quality dimensions measure data quality from different angles. Within each of these
dimensions, a set of data quality rules generated by real business policies can be used toassess the data quality reflected by each dimension [104]. For example, a data quality ruledefined as „the value of date must follow the pattern of DD/MM/YYYY‟ can be used forthe consistency dimension.
Data cleansing is an operation that is performed on the existing data to remove
anomalies and obtain the data collection which is an accurate and unique representationof the mini world [105]. It involves eliminating errors, resolving inconsistencies, andtransforming the data into a uniform format [106]. With the vast amount of data collected,
manual data cleansing is almost impossible as it is time-consuming and prone to errors.
The data cleansing process is complex and consists of several stages which include
specifying the quality rules, detecting data error, and repairing the error [107].
There is no commonly agreed formal definition of data cleaning. Depending on theparticular area in which data cleaning has been applied, various definitions have been
given. The major areas that include data cleaning as part of their defining processes aredata warehousing, knowledge discovery in databases (KDD), and total data/information
quality management (TDQM).
Within the data warehousing field, data cleaning is typically employed when several
databases are merged. Records referring to the same entity are often represented in47different formats in different data sets. Thus, duplicate records will appear in the merged
database. The issue is to identify and eliminate these duplicates. The problem is known
as the merge/purge problem [108]. Other instances of this problem are also referred to asrecord linkage, semantic integration, instance identification or the object identify the
problem in the literature [109]. There are a variety of methods proposed to address thisissue: knowledge bases [110], regular expression matches and user-defined constraints[111], filtering [112], and others [113-115].
In the KDD process, data cleaning is regarded as a first step or a pre-processing step.
However, no precise definition and perspective over the data cleaning process is given
and data cleaning activities are performed in a very domain-specific fashion. For example,
Simoudis et al [116] defined data cleaning as the process that implements computerized
methods of examining databases, detecting missing and incorrect data, and correcting
errors. In data mining, data cleaning is emphasized concerning the garbage in garbage out
principle and its techniques such as outlier detection where the goal is to find exceptions.
For example, the problem of outlier detection where the goal is to find exceptions [117,118] can be used in data cleaning. Total data quality management is an area of interest
both within the research and business communities. From the literature, the data quality
issue and its integration in the business process are tackled from various points of view
[103, 117, 101, 119-122]. It is also referred to as the enterprise data quality management
problem. However, none of the literature refers to the data cleaning problem explicitly.
Most of this work deals with the process management issues from the data quality
perspective, others with the definition of data quality.
Of particular interest in this area, the definition of data quality can help to define thedata cleaning process to some extent. For example, within the model of data life cycles
proposed by Levitin and Redman [120], data acquisition and data usage cycles containthe following series of activities: assessment, analysis, adjustment, and discarding of data.
This series of activities proposed in Levitin and Redman‟s model define the data cleaning
process from the perspective of data quality. Fox et al [119] proposed four data quality48

dimensions of the data, i.e., accuracy, currentness, completeness, and consistency. The
correctness of data is defined in terms of these dimensions. Thus, the data cleaning
process within Fox et all, data quality framework can be defined as the process thatassesses the correctness of data and improves its quality.
With the above in mind and related literature [123, 124], data cleaning must be
viewed as a process that is tied directly to data acquisition and definition or is applied to
improving data quality in an existing system. For example, in Müller and Freytag‟s work,
comprehensive data cleaning is defined as the entirety of operations performed on
existing data to remove anomalies and receive a data collection being an accurate andunique representation of the mini-world [123]. According to Müller and Freytag‟s work,
the three major steps within the data cleaning process are (i) define and determine error
types, (ii) search and identify error instances, and (iii) correct the uncovered errors. Müller
and Freytag include four major steps within the process of data cleaning: (i) auditing data
to identify the types of anomalies reducing the data quality, (ii) choosing appropriate
methods to automatically detect and remove them (specification of data cleaning), (iii)applying the methods to the tuples in the data collection (execution of data cleaning), and(iv) the post-processing or control step where the results are checked and the exception
handling for tuples not corrected within the actual processing are handled.
In this study, to ensure and the reliability and quality of the data used to construct the
MLDB, the following error cleaning methods should be performed.


Keeping the first data recorded in AIS based on the arrival date and time, and deleting
duplicate data points.



Deleting unrealistic voyage data by checking the average voyage speed, which is
calculated by considering the navigation days and distance between two ports. If theaverage voyage speed exceeds the service speed, it is defined as an error and the data
are deleted.

Deleting inappropriate zero values, such as 0-m drafts, null data, and unavailable data.

493.2 Ship Allocation Model
Allocation is the process of assigning product items from the inventory to shippingorders and then fulfilling the shipping orders from appropriate fulfillment sites such as
drop-ship vendors, virtual sites, warehouses, or a retail store. Goods are carried by sea
under a contract of carriage between the shipper and the shipowner. The shipper may
employ a forwarding agent to arrange the transport, while the Shipowner may employ aloading broker to control the allocation of space and advertise the service, and to makethe loading arrangements and prepare documents on the shipowner's behalf.
When a shipper wants to send a particular cargo with a particular ship on a scheduled
service, a "shipping note" for the consignment is completed by the shipper and forwarded
to the shipowner or his agent. This note will have to contain a brief description of thecommodity. The loading broker then compiles a list of the consignments intended forshipment, the booking list. This is sent to the ship to enable the Master to plan the stow
and to the stevedore to arrange the loading. The shipper may receive a "booking note",
which specifies that the carrier reserves space for a specified volume and kind of cargo ina named vessel between named ports. The broker may also issue a "calling forward
notice" to the shipper, advising him of the time and place at which he is to deliver the
goods.
When the cargo is delivered to the warehouse or the ship, a receipt for that cargomust be obtained by the shipper. When the cargo is placed on board, this is called a "mate's
receipt". This receipt acknowledges that the goods have been loaded and have been
properly and carefully handled, loaded, and stowed. If there are any damages to the goods
before loading, this will be recorded on the receipt, and it is no longer "clean".
In some trades, it is customary for the shippers to have a "boat note" following thecargo. When the "boat note" is signed by the cargo officer aboard the ship, it becomes a"mate's receipt". With many shipping companies, it is the practice to give an official"mate's receipt" irrespective of the fact that a boat note may be provided by the shipper.
50

Modern practice is to present a copy of the shipping note as the boat note, which whenendorsed, becomes the "mate's receipt".
Special tally companies are engaged by the shipowner to check or keep a record of
all cargo loaded into and discharged from a vessel. This is an essential part of cargo work
to prevent claims upon the ship for so-called "short" discharge, i.e. when some of the
cargo is missing. It is sometimes customary for the shipper or consignee to provide histally clerks, particularly with cargoes of a straight nature, such as bags, bales, etc.
A copy of the "mate's receipt" will be returned to the shipowner, so that a "bill of
lading" can be issued to the shipper. The "bill of lading" acknowledges that the goods
have been "shipped in apparent good order and condition" if the "mate's receipt" is clean.
Otherwise, comments are transferred to the "bill of lading". This document is issued under
all forms of shipping, scheduled or not. The complete list of cargo loaded, as compiled
from the "bills of lading" form the "manifest" of the ship. Customs regulations at mostports require at least one copy of the manifest and copies are also required for stevedoresat discharging ports.
While cargoes are in transit, they may be sold so that the goods change ownership.
Such a sale will be represented by the "bill of lading" changing hands. At the port ofdischarge, the consignment will be handed over to the party presenting the original "billof lading".
The basic concept of the ship allocation model is shown in Fig. 3.1 which highlights
the two important steps for realizing the objectives: first, developing a ship allocation
model, and second, carrying out simulations using the ship allocation model. A ship
allocation model can reproduce actual ship allocation. Input and output data of the ship
allocation model are shown in Table 3.1.

51

Fig. 3.1 Basic concept of ship allocation
Table 3.1. Input and output of ship allocation model
Trade conditions: fuel price, trade volume between the ports.

Input

Allowable ships specifications: DWT, LOA (m), B (m), D (m), d (m),service speed (knot), horsepower
Constraints of the canal and ports: Max DWT, Max LOA (m), Max B(m), and Max D (m), etc.
Number of allowable ships

OutputShip allocation of all ships

52

3.2.1 Configuration of the ship allocation model
To realize actual ship allocation conditions, we develop three distinct models—theshipper, shipowner, and operator models.


The shipper model issues a request for cargo transportation between two or more
ports. The shipper model is defined using cluster analysis. •The shipowner model estimates the shipment days, amount of cargo, andoperating cost in response to the cargo transportation requests. The shipownermodel is defined using deep learning analysis.

The operator model requests all shipowner models to estimate shipment costs,
cargo volume, and transport time based on shipper requests; then, based on theanswer from the shipowner model, the operator model decides on a ship for
cargo transport. A detailed explanation of ship allocation and confirmation of
the proposed models is shown in Section 5.

3.3 Simulation Using Ship Allocation ModelIn the ship allocation model, a competitive ship is allocated to a prioritized route.
Moreover, the following data can be set freely in executing the simulation:


Future scenario (fuel price, trade volume between ports)New ship specifications



Number of ships (freely selectable by the operator)Constraints of ports and canals

With these characteristics, we can execute the following simulations:(1) Examination of the supply-demand balance of various ships
In our system, supply is defined as a ship allocation only using existing ships, anddemand is defined as a ship allocation in which all the ships can be used freely for53

cargo transportation. Therefore, by changing the number of ships that can be used in
the simulation, we can estimate the supply-demand balance.
(2) Examination of effective ship specifications
As discussed in the previous section, we can change the specifications freely. Then,by changing ship specifications and simulating ship allocation, we can understandthe demand for various kinds of ships. Therefore, by executing this simulation, we
can examine effective ship specifications and the kinds of ships that are attractive for
operation on the intended routes.
(3) Influence of economic situations on demand.
In our system, port constraints can be changed. Moreover, fuel prices and tradevolume between the ports can be freely changed. By forecasting such a future
situation using a ship allocation model, we can understand logistics and demand
results. Moreover, we can understand the kinds of ships that will operate effectively
on intended routes in the future. In this paper, we take bulk carriers that operate
between Australia and Japan as an example. Detailed simulations are shown in
Section 6.

54

Chapter 4
Development of Marine Logistics Database
As mentions in the previous section, the objective of this study is to develop a support
system of ship basic planning using Marine Logistics Big Data. There are 3 important
points to realize the objectives; Marine Logistics Database (MLDB), Ship Allocation
Model, and Simulations. Below is the detailed description of the Marine Logistics
Database (MLDB)

4.1 Definition of Marine Logistic Database (MLDB)
The marine logistic database is defined as an integration of maritime Big Data that is
structured and managed into a relational database and denoted as MLDB. The MLDB
consists of the latest marine logistics data, i.e., operation information from AIS, ship, port,route, and international trading information, as shown in Fig. 4.1. The data are managed,
integrated, and structured to derive valuable insights from information buried in marine
logistics data.

Fig. 4.1 Basic concept of MLDB

55

4.2 Development of MLDBMarine logistic database (MLDB) is developed by integrating the following big data
i.e. operation information from AIS, ship, port, route, and international trading
information as an input. The development of MLDB is described as follows:
4.2.1 Input of MLDB
A. Automatic Identification System (AIS) Data
The Automatic Identification System (AIS) is a worldwide automatic positioning
system based on fitting small transponders to vessels that continuously transmits a signal.
This alerts other vessels and shore stations with AIS receivers to the presence of thatvessel. The position information is supplemented with additional information about thevessel. The signals and accompanying information can then be received by any vessel,land station, or satellite fitted with an AIS receiver and is then typically displayed on ascreen using interactive chart-plotting software [125].

Fig. 4.2 AIS Data Transponder, [125]56International maritime law requires AIS transponders to be fitted aboard international
voyaging ships with a gross tonnage of 300 tonnes or more, and on all passenger ships
regardless of their size. Given its visibility and safety advantages many smaller vessels
also voluntarily install AIS units. In many countries, no license is required to purchaseand operate either transponders or receivers. The result is that AIS is used almost
universally in the worldwide commercial maritime industry and increasingly so in theleisure marine sector. Not all vessels can be tracked by AIS. Naval and security ships
generally prefer not to be tracked when on active duty, and cases are regularly reportedof commercial vessels underway with their transponders turned off for unspecified
reasons.
 A brief history of AIS
It’s not entirely clear who it was that came up with the first AIS vessel tracking
and identification system, but like so much other technology that we take for
granted these days, it came out of the introduction of GPS for civilian purposes,
which achieved global coverage in the early 1990s. It was then the integration of
GPS time and position data with long-standing VHF radio technology that enabled
the development of AIS.

57

Fig. 4.3 AIS Conceptual Operation View, [125]In its early years, the primary use of AIS was as a ship-to-ship anti-collisionsystem for use in poor visibility and at night, in support of radar and conventional
watch keeping. Over time the amount of information that could be transmitted inthe VHF signal grew and its usefulness increased. In 2002 it finally went global
when the IMO in its landmark SOLAS convention mandated that all passenger
ships and other commercial vessels over 300 GT should carry Class-A AIStransceivers. At the time this affected around 100,000 ships, but since then use has
expanded as the cost of transceivers has fallen and both compulsory and voluntary
adoption has increased.
Originally developed as a short-range identification and vessel tracking system, atthe start of the 21st century it was discovered that AIS transmissions could bereceived at ranges of up to 400km above the surface of the earth, whereas on thesurface the maximum effective distance is around one-tenth of that. Thisrevolutionized AIS, taking it from a coastal and ship-to-ship tracking application
to a vessel management system with global coverage. However, the challenge forsatellite operators looking to develop this opportunity now is managing theenormous volumes of data that this creates for individual satellites eachmonitoring thousands of square kilometers of ocean.
The AIS format uses TDMA radio access that allows for just 4,500-time slots per
minute. A one-time slot equates to a single vessel transmission. Any more thanthat and the individual signals start to interfere with each other, corrupting the data
held within. The terrestrial AIS infrastructure with its short-range and higher
density does not have the same capacity problems. The satellite developers are,however, working on ways of receiving and processing incoming data at fasterrates and rapid advances are being made.

58

Fig. 4.4 Merchan Ship AIS Diagram Block, [125]There is no perfect vessel tracking system, but AIS is becoming increasingly
effective as accuracy and refresh rates get even better. Its ability to interface withother detection sources makes it an important component of integrated navigation
and warning systems, and the addition of supplementary environmental andsituational data makes it yet more versatile. Without a doubt, AIS is now one of
the most valuable information sources available for anyone involved in themaritime sector.
 AIS in PracticeThere are two classes of AIS transponders: A and B. Broadly speaking, the higher
specification class A is mandated for commercial vessels, while the lower
59

specification class B is intended for smaller, mostly leisure, vessels. Capabilities
and prices vary greatly between the two classes. Information from Class A units
is also prioritized over that from Class B equipment.
Class A: Class A transponders are mandated under international SOLAS
regulations for all international voyaging ships with a gross tonnage of 300 tonnes
or more, and on all passenger ships regardless of size. They transmit continuously
at 12.5 Watts and have a horizontal range of up to 40nm. They use SOTDMA(Self-Organized TDMA) technology so that each automatically adjusts its
transmissions to avoid interfering with others within range. In areas with highdensity shipping, the system also shrinks the area of coverage when necessary toensure that the system isn’t overloaded.

Fig. 4.5 AIS Class A, [125]Class B: Class B transponders were developed to give smaller vessels voluntaryaccess to the benefits of the AIS system. They transmit every 30 seconds at 2
Watts and the horizontal range is line-of-sight. They use CSTDMA (Carrier Sense
TDMA) technology that checks for Class A transmissions before sending its
signal. Class B information is therefore only broadcast when there is sufficient60

space on the AIS channel. Class B AIS capability is increasingly being included
in low-cost chart plotters and multifunction displays to overlay the information onelectronic charts.

Fig. 4.6 AIS Class B, [125] AIS Data TypeThe availability of the data provided by the AIS depends on the AIS type. The
characteristics of data recorded by the AIS is described as follows:
Class A: Provides three types of information:
1. Fixed, or static information, which is entered into the AIS on installation andneeds only be changed if the ship changes its name or undergoes a major
conversion from one ship type to another. Includes data such as:o

MMSI (Maritime Mobile Service Identity)oCall sign and name of the vesselo

IMO NumberoLength and beamoType of shipoLocation of Position-fixing antenna61

2. Dynamic information, which, apart from navigational status information, is
automatically updated from the ship sensors connected to AIS. Includes:oShip’s position with accuracy indication and integrity statuso

Position Timestamp in UTCo

Course over ground (COG)o

Speed over ground (SOG)oHeadingoNavigational status (e.g. underway by engines, at anchor, engaged infishing, etc)oRate of turn (ROT)

3. Voyage-related information, which might need to be manually entered andupdated. Such as:o Ship’s draughto Hazardous cargo (type) (e.g. DG (Dangerous goods), HS (Harmful
substances)o or MP (Marine pollutants)o Destination and ETAo Route plan (waypoints) (at the discretion of the master)Class B: Class B transponders transmit only static information every six minutes.
This should include:o MMSI (Maritime Mobile Service Identity)o Call sign and name of the vesselo Length and beamo Type of vessel
The original purpose of AIS was to reduce the risk of vessels colliding with eachother in poor visibility. However, the combination of satellite AIS allowing near-global
62

coverage and the ability to access AIS data online has now made it a valuable resource
for anyone wishing to monitor individual or groups of vessels, different classes of vessels,
or volumes of total traffic in certain areas or the whole world.
As well as ships’ officers, AIS data feeds with mapping overlays are therefore usedby vessel owners and operators to monitor and manage their fleets, as well as port
managers and service providers, shippers, maritime security providers, insurers, othermaritime professionals, marine intelligence analysts, government agencies, economists,
academics, and family and friends of crew members.
Users vary greatly in how they use the information. Many vessels owners use it
simply to see where their vessels are at any given time while sitting at their desks orindeed on the beach or at home. Port managers can use the information to view incoming
vessels and their latest expected times of arrival. Yachts off cruising or racing can betracked by family and friends seeking peace of mind that all is well. On a bigger scale,academics and economists can analyze AIS data on a global scale to identify patterns andchanges in trade flows and volumes, while environmentalists can use it to identify areasthat might be at risk from excess marine traffic.
AIS today provides anyone who wants to see it with the most complete view availableof the activity that takes place 24/7/365 on the world’s oceans, seas, and inland waterways,
together with a treasure trove of information on the size, type, and often cargoes of thevessels themselves. Its evolution over the years from a simple collision-avoidance system
is truly remarkable.
In the last few years, new utilization of AIS such as logistics and transport economy
have emerged. AIS data can be used in the various mapping of marine traffic to be usedin the various simulation. Further, AIS has a wide interpretation, as it can be used solely,or can be combined to gain deeper insights. Therefore, AIS-based studies can benefit
63parties, from official authorities to private parties such as cargo owners or shipping
companies [126].
In this study, AIS data was acquired from IHS Markit. AIS data include the ship
name, movement timestamp, indicated sailing speed, indicated draught, andlatitude/longitude coordinates, etc. The illustration of AIS data is shown below.

Fig. 4.7 AIS Data Illustration, [20]B. Ship Operation
Besides AIS data, ship operation data was acquired from Market Intelligence
Network (MINT) managed by IHS Markit. It is possible to obtain the current position ofeach ship, the ship name, IMO number, indicated draught, arrival/departure dates, andarrival/departure ports for 2014 until 2017. As shown in Table 2.2, typical ship operation
data is comprehension from AIS data. It consists of the longer-range timestamp in themanner of ship position or ship movement defined by its sailing speed. In the same size
ratio of data, ship operation data enclosed more information rather than AIS data.

64

Table 4.1 Typical Data of Ship Operation Data, [20]ArrivalVesselVesselVesselArrivalArrivalMax

Previous Port

Port

Name

Type

IMO

Date

Draught

Draught

Departure Dtae

Kashima

SHIP ABC

9355147

18

18,105

Kashima

SHIP ABC

9355147

17,2

18,105

Kashima

SHIP ABC

9355147

18

18,105

Kawasaki

SHIP ABC

9355147

18,1

18,105

Kisarazu

SHIP ABC

9355147

18

18,105

Kisarazu

SHIP ABC

9355147

15,418,105

Kisarazu

SHIP ABC

9355147

15,418,105

Kisarazu

SHIP ABC

9355147

17,7

18,105

Muroran

SHIP ABC

9355147

15,1

18,105

Muroran

SHIP ABC

935514715,918,105

Oita

SHIP ABC

9355147

16,7

18,105

Oita

SHIP ABC

9355147

17,6

18,105

Oita

SHIP ABC

9355147

17,6

18,105

Oita

SHIP ABC

935514717,518,105

Oita

SHIP ABC

9355147

16,518,105

Oita

SHIP ABC

9355147

15,2

18,105

2015-12-02
01:55:46
2014-03-07
23:10:20
2013-01-31
07:20:31
2017-08-04
05:41:07
2016-01-12
07:56:03
2015-04-19
03:10:48
2014-10-25
01:40:22
2014-09-01
14:55:47
2015-09-26
10:25:35
2013-08-09
05:45:29
2015-08-10
21:11:01
2015-03-01
09:10:36
2014-01-19
02:55:20
2013-12-08
20:55:18
2013-10-19
06:55:23
2013-05-25
09:45:25

2015-11-17
05:26:02
2014-02-18
02:10:20
2013-01-17
05:42:29
2017-07-22
13:23:11
2015-12-30
02:57:05
2015-04-04
11:40:29
2014-10-06
20:40:33
2014-08-19
20:18:09
2015-09-09
07:25:48
2013-07-23
22:30:27
2015-07-22
01:25:33
2015-02-02
20:25:30
2014-01-04
15:25:18
2013-11-25
06:25:19
2013-10-01
22:00:33
2013-05-05
07:20:21

65

C. Ship Data
The ship data used in this study was collected from Sea-web Ships. Ship data consistsof ship name, IMO number, built year, flag, and principal dimensions, such as DWT,
length, draught, and breadth. Figure 4.8 shown the illustration of ship data, and typicalship data collected in MLDB is shown in Table 4.2.

Fig. 4.8 Illustration of Ship Data, [20]Table 4.2 Typical Data of Ship Data
Vessel IMO

Name of Ship

MMSI

9592446

AANYA

373434000

Deadweight Year Class
1796282012

LR

HHIC-Phil Inc

Shipbuilder

Service Speed Length Breadth Draught
14,5

292

45

18,2

9583897

AARGAU

269108000

32790

2010

BV

Universe Shipbuilding Yangzhou

13,7

179,9

28,4

10,15

9087738AARTI PREM

636014966

69087

1994

NK

Imabari Shbldg - Marugame

14,5

224,9832,213,2959592458

AASHNA

373916000

1795232012

LR

HHIC-Phil Inc

14,5

292

45

18,2

9019535

AAZAM

422535000

1852

1990

AS

Sungkwang Shipbuilding Co Ltd

12

70,49

12,51

4,312

9571040

ABDALA

370533000

34938

2011

LR

Shanghai Shipyard Co Ltd

13,7

179,9

28,4

10,8

7116781

ABDUL M

671359000

6370

1972

DR

Viana Do Castelo

11

101,48

15,97

7,217

9132923

ABDULLAH

405000132

45653

1996

NK

Tsuneishi Shbldg - Fkym - earl

14,9

185,74

30,4

11,62

8902929

ABDULLATIF

312793000

13790

1992

NK

Szczecinska Stocznia SA

12,8

143,7

20,6

8,43

9334882

ABIGAIL N

636014327

2974302009

NV

Universal Shbldg - Tsu

14,3

327

55

21,4

9213399

ABILA

422034700

75249

2001

X

Samho Heavy Industries Co Ltd

14,8

225

32,25

14,15

8912261

ABK TRADER

312338000

28452

1991

NK

Imabari Shbldg - Imabari

13,7169,03

27,2

9,745

9006643

ABM DISCOVERY 353527000

39110

1992

NK

IHI - Tokyo

13

180,8

30,5

10,93

8400971

ABM NAVIGATOR 312320000

11,469

42183

1987

NK

Sasebo Sasebo

14

185,9130,419481702

ABML EVA

229068000

1066592012

GL

STX Dalian Shipbuilding Co Ltd

14,5

253,5

43

13,6

9224738

ABML GRACE

229567000

1723192002

BV

Daewoo Shipbuilding & Marine

14,5

289

45

17,82

66

D. Port Data
The port data consists of port name, port specification/dimension, latitude/longitude
information, and handled cargo types. Port data was used in the previous studies and thisstudy was collected from Sea-web Ports. Fig 4.9 shown the illustration of port data, andtypical port data collected in MLDB is shown in Table 4.3.
Country NamePort Name

Latitude.Longitude

Cargo Handling

Other Information

Berth Information

Annual tonnage etc.

Fig. 4.9 Illustration of Port Data, [19]Table 4.3 Typical Data of Port Data
Port NameCountry Latitude Longitude Max DWT Max Draft Max LOA Max Breadth Annual Tonnnage Port Number UN Locode

Abbot Point

Australia

Cargo Type148,0833

200000

17,6

300

50

14443487

PO3978

AUABP

Coal

Australia -35,0333 117,8833

67000

11,5

225

33

3501077

PO1046

AUALH

Grain, Others

Brisbane

Australia -27,3833 153,1666

148160

14,2

294

46

31877104

PO1052

AUBNE

Iron ore, Grain, Coal, Others

Bunbury

Australia -33,3166 115,6333

80000

11,6

234

32,2

13866969

PO1054AUBUY

OthersDampier

Australia -20,6666

250000

19,5

340

55

165025204

PO1060AUDAM

Iron ore, Others

Esperance

Australia -33,8666 121,8833

203000

18

300

50

10000000

PO3709AUEPR

Iron Ore, Grain

Geraldton

Australia -28,7666

114,6

80000

12,8

225

32,5

9005508

PO1068AUGETIron Ore, Grain

Gladstone

Australia -23,8166

151,3231850

17,9

315

55

55602406

PO1069AUGLT

Grain, Coal, Others

Hay Point

Australia -21,2666 149,3166

231851

17,5

315

56

99500000

PO1071

AUHPT

Coal

LucindaAustralia -18,5333 146,3333

70000

12,3

230

32,2

575497

PO1075AULUC

OthersAlbany (Australia)-19,85116,7

Milner Bay

Australia -13,8833

136,45

50000

12,2

200

32,6

3893821

PO1079AUMIB

Iron Ore, Others

Newcastle

Australia -32,9166

151,8

232000

16,2

300

55

114575744

PO1080AUNTL

Grain, Coal, Others

Newcastle (Australia) Australia -32,9166

151,8

232000

16,2

300

55

114575744

PO1080AUNTL

Grain, Coal

118,5833

260000

19,2

330

55

199002079

PO1083

AUPHE

Iron ore

150,9206306

16

300

50

24036000

PO1084

AUPKL

Coal

145,3833

130000

15,3

245

38,5

2200000

PO1085

AUPLA

Iron Ore

Australia -20,6166 117,1833

340000

19,5

335

60

54626968

PO1089AUPWLIron Ore

Australia -12,6666

85000

11,7

256

35,4

22111499

PO1100AUWEIOthersPort Hedland

Australia

Port Kembla

Australia -34,4666

Port Latta

Australia

Port Walcott
Weipa

-20,3

-40,85

141,8567

E. Route Data
The route data comprise arbitrary route data. Nautical mile distance for each voyageroute obtained from IHS Fairplay. Figure 4.10 shown the illustration of route data andtypical route data collected in MLDB shown in Table 4.4.

Departure Port

Distances

Route

Arrival Port

Other routes choices

Fig. 4.10 Illustration of Route Data, [21]Table 4.4 Typical Data of Route Data
Route Number Arrival Port Name Departure Port Name

Route

Distance Speed Days

Via

RN0053Chiba

Gladstone

ChibaGladstone

3793

14

11,29 DIRECT

RN0125

Chiba

Abbot Point

ChibaAbbot Point

3598

14

10,71 DIRECT

RN0133Chiba

Dampier

ChibaDampier

3665

14

10,91 DIRECT

RN0204

Fukuyama

Gladstone

FukuyamaGladstone

3856

14

11,48 DIRECT

RN0258

Fukuyama

Abbot Point

FukuyamaAbbot Point

3644

14

10,85 DIRECT

RN0335

Fukuyama

Dampier

FukuyamaDampier

3585

14

10,67 DIRECT

RN0538

Hibikinada

Gladstone

HibikinadaGladstone

3970

14

11,82 DIRECT

RN0564

Hibikinada

Abbot Point

HibikinadaAbbot Point

3756

14

11,18 DIRECT

RN0615

Higashi-Harima

Gladstone

Higashi-HarimaGladstone

3832

14

11,4 DIRECT

RN0637

Higashi-Harima

Abbot Point

Higashi-HarimaAbbot Point

3620

14

10,77 DIRECT

RN0680Higashi-Harima

Dampier

Higashi-HarimaDampier

3568

14

10,62 DIRECT

RN1075

Kanda

Abbot Point

KandaAbbot Point

3653

14

10,87 DIRECT

RN1168

KashimaDampier

KashimaDampier

3714

14

11,05 DIRECT

RN1190KashimaGladstone

KashimaGladstone

3791

14

11,28 DIRECT

RN1330KawasakiGladstone

KawasakiGladstone

3778

14

11,24 DIRECT

RN1338

KawasakiDampier

KawasakiDampier

3650

14

10,86 DIRECT

RN1340Kawasaki

Abbot Point

KawasakiAbbot Point

3582

14

10,66 DIRECT

RN1434

KinuuraAbbot Point

KinuuraAbbot Point

3587

14

10,68 DIRECT

68

F. Trade Data
The trade data comprise the transported cargo amount between countries. Trade data
include the exporter/importer countries name, trade period, trade flow, code, trade value,
net weight (kg), etc. The illustration of UN Comtrade data selection is shown in Fig. 4.11,
and the typical trade data collected in MLDB is shown in Table 4.5.

「2014、AUSTRALIA→JAPAN、COAL」

Fig. 4.11 Illustration of Trade Data, [22]Table 4.5 Typical Data Trade from UN Comtrade
Iron Ore
Period Trade Flow Reporter Partner Code2014

ImportJapan

Trade Value

Australia 2601 $9.001.321.700

NetWeight (kg) Quantity Unit Trade Quantity
82.884.716.192

882.884.716.192(Ton)
82.884.716

Coal
Period Trade Flow Reporter Partner Code2014

ImportJapan

Trade Value

NetWeight (kg) Quantity Unit Trade Quantity

Australia 2701 $12.660.422.478 119.139.855.456

8

(Ton)

119.139.855.456 119.139.855

Grain
Period Trade Flow Reporter Partner Code

Trade Value

2014

ImportJapan

Australia 1001

$316.168.354

NetWeight (kg) Quantity Unit Trade Quantity
928.869.984

8

928.869.984

928869,984

2014

ImportJapan

Australia 1003

$190.139.529

646.754.016

8

646.754.016

646754,016

2014

ImportJapan

Australia 1004

$5.944.581

13.918.000

813.918.000

13918

2014

ImportJapan

Australia 1005

$577.148911.0008911.000

911

2014

ImportJapan

Australia 1006

$27.925.641

38.401.000

838.401.000

38401

2014

ImportJapan

Australia 1007

$293.263

301.0008301.000

301

2014

ImportJapan

Australia 1008

$407.231

313.000

8313.000

313

1.629.468.000(Ton)

1.629.468

69

4.3 Data Structure of MLDB
To more easily extract valuable information, we defined a structure for the MLDB
and modified unstructured data into a relational database. For example, by integratingship and port data with operation data, some information related to a ship’s operational
state can be analyzed (e.g., berthing, anchoring, or sailing). All available marine logistics
data are integrated into a one-to-many relationship. The data structure of MLDB is shownin Fig. 4.12.
ROUTE DATAOPERATION DATASHIP DATAIMO Number

1

Ship Name

1

PARENTN

IMO Number

N

Route Number

Route (Via)Route Number

Depart. Port Name(MMSI) Number

Departure Draft

Arrival Port Name

Years

Arrival Draft

Deadweight

Service Speed

A/D Port Number

LengthDistance

TRADE DATANNPort Number

Departure Date

PeriodBreadth

Arrival Date

Reporter

Draft

Operation Number

1

1

PORT DATA

Partner
Commodity Code
Trade Volume

Ex-NameCargo TypeA/D Port NameEngine Type

Cargo Volume

Country

Ship TypeShip Operator
CHILD

Shipbuilder
1

StatusOperation Number
Departure Port
Arrival Port Name

GRANDCHILD
Operation Number

N

Departure Draft

N

1

A/D Port Number

LatitudeCargo Type

Longitude

Net Weight (kg)Annual Tonnage

Trade Quantity

Max Draft
Max Length
Max Breadth
Max DWT

Date

Arrival Draft

Time

Departure Date

United Nation (UN)LocodePosition

Arrival Date

Cargo Type

1N

One-to-Many
Relationship 32)

Fig. 4.12 Data Structure of MLDB

4.4 Data Cleaning
Essentially, big data utilization encounter issues of data quality. Some data may beflawed caused by sensor faults or mistakes made during manual entry. Extensive validity
check considered to proper routine [127]. To ensure and the reliability and quality of thedata used to construct the MLDB, the following error cleaning methods were performed.

70

4.4.1 Duplicated Data
Duplication in recorded data of arrival port and/or departure port. The data is definedas duplicated data managed by taking the earliest arrival date and the latest departure date,the data in between is deleted. The example of duplicated data is shown in Table 4.6.
Table 4.6 Example of Duplicated Data at Oita PortPort

Country Arrival Date Days Ago Arrival Draught Departure Draught

Oita

Japan

08/04/2015

344

7,4

7,4

Cristobal

Panama07/04/2015

345

7,4

7,4

Oita

Japan

07/04/2015

345

7,4

7,4

Cristobal

Panama07/04/2015

345

7,4

7,4

Oita

Japan

07/04/2015

345

7,4

7,4

Cristobal

Panama07/04/2015

345

7,4

7,4

Oita

Japan

07/04/2015

345

7,4

7,4

Cristobal

Panama07/04/2015

345

7,4

7,4

Oita

Japan

07/04/2015

345

7,4

7,4

Cristobal

Panama07/04/2015

345

7,4

7,4

Oita

Japan

07/04/2015

345

7,4

7,4

Cristobal

Panama06/04/2015

346

7,4

7,4

Oita Quarantine AnchorageJapan

06/04/2015

346

7,4

7,4

Tonda

Japan

04/04/2015

348

6,8

6,8

Cristobal

Panama02/04/2015

350

10,8

6,8

4.4.2 Unrealistic Ship Movement Data
Too fast sailing speed of the ship in the manner of departure and arrival date, anddistance between ports. Thus, misreported location or arrival date will result fromunrealistic voyage data. The solution is deleting unrealistic voyage data by checking the
average voyage speed, which is calculated by considering the navigation days anddistance between two ports. If the average voyage speed exceeds the service speed, it isdefined as an error and the data are deleted.

71

4.4.3 Unrealistic Ship Draught Data
Recorded data contains zero (null) draught data, or draught data is not changed during
operation considered to be unrealistic ship draught data. For this case of error, the draught
was corrected with average draught data from the same ship size category.

4.5 Generating Cargo Information
Cargo information on an operating ship are important for demand forecasting andunderstanding the ship’s use. However, such information does not exist in AIS data.
Therefore, we estimated the cargo type and volume of each operation. In the case of abulk carrier, the cargo type is selected from three types: iron ore, coal, and grain andminor bulk (MB). The estimation methods used in this study are described as follows.
4.5.1 Checking Data Reliability
Confirmation of data’s reliability is required for a good cargo volume estimation. In
our study, data reliability was evaluated by checking the draft rate di by using Eq. (1)
𝑑

(𝑚)

(1)

𝑑𝑖 = 𝑑 𝑠𝑎𝑖𝑙.𝑖 (𝑚)𝑚𝑎𝑥.𝑖Where daily (m) is the sailing draught, maxi (m) is the ship maximum draught, and di is thedraught rate.

Distribution of draft rate (Australia-Japan)

120%
100%

Draft rate

80%
60%
40%

Iron ore
Coal
Grain & Others

20%
0%
0

50000

100000 150000 200000
Ship Size (DWT)

250000300000350000Fig. 4.13 Bulk Carrier Draught Rate in Australia-Japan 201472

Table 4.7 Bulk Carrier Status based on its Draught Rate
Draft Rate [%] Arrival>Departure Arrival=Departure Arrival<Departure100+

Unknown

Unknown

Unknown

65~100

Loading

Unknown

Unknown

45~65

Unknown

Unknown

Ballast0~45

Unknown

Unknown

Unknown

4.5.2 Estimating Cargo Type Using Port Data
By identifying the cargo type from port data, the cargo of each operation could be
estimated. As shown in Table 4.8, cargo type estimation was conducted by checking the
combination of cargo from the arrival and departure ports. In the case of operation fromPort A to Port D, the only common cargo is coal. Therefore, the cargo type was estimatedto be coal. In contrast, in the case of operation from Port B to Port D, there are two
common cargos: coal and iron ore. In this case, cargo type was defined as multi-cargo
and decided using the ship size.
Table 4.8 Estimation Cargo Type Using Port Data
PORTPort A(Coal, Grain & MB)
Port B(Coal, Iron Ore)Port C(Iron Ore, Grain & MB)

Port D(Coal, Iron
Ore)

Port E(Coal, Iron Ore, Grain & MB)

Port F(Coal, Iron
Ore)

Coal

Coal, Grain & MB

Coal

Coal, Iron Ore

Coal, Iron Ore

Coal, Iron Ore

Iron Ore

Iron Ore, Grain & MB

Iron Ore

4.5.3 Estimating Cargo Type Using Ship Size
If two or more common cargo types exist in port data, the cargo types were estimatedusing ship size. Since ship size and cargo type are closely related, the remaining operation
could be estimated. The flowchart of estimation steps using port data and ship size isshown as follows:73Fig. 4.14 Estimating Cargo Using Port Data (Single Cargo)

Fig. 4.15 Estimating Cargo Using Port Size (Multi Cargo)74

Fig. 4.16 Estimating Cargo Using Port Size (No Common Cargo)
4.5.4 Estimating Cargo Volume
Ship data has information on the deadweight and maximum draft of the target ship,
while AIS data has information about the sailing draft. The cargo volume was estimatedusing Eq. (2).

(2)
where Vi (ton) is cargo volume, DWTi is deadweight, and di is the draft rate. As explainedin the previous section, the operation conditions of ships were defined as loading, ballast,and unknown. In the unknown condition, cargo volume was estimated by considering the
average draft of ships of the same size operating on the same route.

75

4.6 Confirmation of Cargo Estimation of MLDB
To verify the cargo estimation in Section 4.6, we compared our results with actual
trade value from UN Comtrade data, using bulk carriers operating from Australia andBrazil to Japan, Australia and Brazil to Korea, and Australia and Brazil to China. The
confirmation results of cargo estimation for each route are shown as follows:
4.6.1 Cargo Estimation from Australia-Brazil to JapanThe cargo estimation exported from Australia to Japan, and Brazil to Japan is shownin the following table. The results are shown based on the several scenarios; (1) All
operation (including error data), (2) All operation + error cleaning, (3) All operation +error cleaning + draft cleaning. The results are shown as follows:
Table 4.9 Result of Estimation Cargo 2014 (Australia~Japan)
Scenario 1

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

15465577

262

12351717

125%

Iron Ore

85150411

448

82884716

103%

Coal

115836800

1145

119749649

97%

TOTAL

216452788

1855

214986082

101%

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

12191371

243

12351717

99%

Iron Ore

83958690

440

82884716

101%

Coal

116641588

1135

119749649

97%

TOTAL

212791649

1818

214986082

99%

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

11624683

243

12351717

94%

Iron Ore

78742212440

82884716

95%

Coal

108976830

1135

119749649

91%

TOTAL

199343724

1818

214986082

93%

Scenario 2

Scenario 376

Distribution of draft rate (Australia-Japan)

120%
100%

Draft rate

80%
60%
40%

Iron ore
Coal
Grain & Others

20%
0%

0

50000

100000150000200000
Ship Size (DWT)

250000300000350000Fig. 4.17 Distribution of Draught Rate (Australia~Japan)
Table 4.10 Result of Estimation Cargo 2014 (Brazil~Japan)
Scenario 1

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others306731952

2698556

114%

Iron Ore

36467312

203

36993022

99%

Coal

0

0

0

0%

TOTAL

39534631

255

39691578

100%

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

272734945

2698556

101%

Iron Ore35244640

198

36993022

95%

Coal

0

0

0

0%

TOTAL

37971989

243

39691578

96%

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

250487045

2698556

93%

Iron Ore34122409

198

36993022

92%

Coal

0

0

0

0%

TOTAL

36627280

243

39691578

92%

Scenario 2

Scenario 377

Distribution of draft (Brazil-Japan)
120%

draft rate

100%
80%
60%
40%

Iron Ore

20%
0%Grain & Others

0

100000200000300000400000500000

Ship Size (DWT)

Fig. 4.18 Distribution of Draught Rate (Brazil~Japan)

4.6.2 Cargo Estimation from Australia-Brazil to Korea
The cargo estimation exported from Australia to Korea, and Brazil to Korea is shownin the following table. The results are shown based on the several scenarios; (1) All
operation (including error data), (2) All operation + error cleaning, (3) All operation +error cleaning + draft cleaning. The results are shown as follows:
Table 4.11 Result of Estimation Cargo 2014 (Australia~Korea)
Scenario 1

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

7132344130762462094%

Iron Ore

50317540

248

50949931

99%

Coal

50411964

359

54996867

92%

TOTAL

107861848

737

113571418

95%

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

6873369125762462090%

Iron Ore49902348

246

50949931

98%

Coal

49894285

356

54996867

91%

TOTAL

106670002

727

113571418

94%

Scenario 278

Scenario 3
Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

6558950125762462086%

Iron Ore48872168246

50949931

96%

Coal

47065859

356

54996867

86%

TOTAL

102496977

727

113571418

90%

Table 4.12 Result of Estimation Cargo 2014 (Brazil~Korea)
Scenario 1

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

4244416514261497

100%

Iron Ore

15899091

71

15849483

100%

Coal

0

0

0

0%

TOTAL

20143507

122

20110980

100%

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

4244416514261497

100%

Iron Ore

1571122770

15849483

99%

Coal

0

0

0

0%

TOTAL

19955643

121

20110980

99%

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

3838960514261497

90%

Iron Ore

15206546

70

15849483

96%

Coal

0

0

0

0%

TOTAL

19045506

121

20110980

95%

Scenario 2

Scenario 3

79

Distribution of draft rate (Australia-Korea)
120%

Draft rate

100%
80%
60%
Iron Ore40%

Coal
Grain & Others

20%
0%

0

50000

100000 150000 200000
Ship Size (DWT)

250000300000Fig. 4.19 Distribution of Draught Rate (Australia~Korea)

Distribution of draft rate (Brazil-Korea)
120%

Draft rate

100%
80%
60%

40%

Iron Ore

20%
0%

0

100000200000300000
Ship Size (DWT)

400000500000Fig. 4.20 Distribution of Draught Rate (Brazil~Korea)

4.6.3 Cargo Estimation from Australia-Brazil to China
The cargo estimation exported from Australia to China, and Brazil to China is shownin the following table. The results are shown based on the several scenarios; (1) All
operation (including error data), (2) All operation + error cleaning, (3) All operation +error cleaning + draft cleaning. The results are shown as follows:80

Table 4.13 Result of Estimation Cargo 2014 (Australia~China)
Scenario 1

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

50463648

603

46754508

108%

Iron Ore508335211

2760

548354530

93%

Coal

79836626

732

79595444

100%

TOTAL

638635485

4095

674704481

95%

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

43075896

543

46754508

92%

Iron Ore

491943119

2671

548354530

90%

Coal

78141995

718

79595444

98%

TOTAL

613161010

3932

674704481

91%

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

39706038

543

46754508

85%

Iron Ore475288948

2671

548354530

87%

Coal

74804274

718

79595444

94%

TOTAL

589799260

3932

674704481

87%

Scenario 2

Scenario 3

Distribution of draft rate (Australia-China)
120%

100%

Draft rate

80%
60%

Iron Ore40%

Coal
Grain & Others

20%

0%

0

100000200000300000400000

Ship Size (DWT)

Fig. 4.21 Distribution of Draught Rate (Australia~China)
81

Table 4.14 Result of Estimation Cargo 2014 (Brazil~China)
Scenario 1

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

46708026

573

48840289

96%

Iron Ore

138113282

681

134450605

103%

Coal

0

0

0

0%

TOTAL

184821308

1254

183290894

101%

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

45552261

559

48840289

93%

Iron Ore

133886109

662

134450605

100%

Coal

0

0

0

0%

TOTAL

179438370

1221

183290894

98%

Cargo Type

Cargo Volume

Amount of Ship

UN (T)

Ratio

Grain & Others

41883160.75

559

48840289

86%

Iron Ore130320675.8

662

134450605

97%

Coal

0

0

0

0%

TOTAL

172203837

1221

183290894

94%

Scenario 2

Scenario 3

Distribution of draft rate (Brazil-China)
120%

DSraft Rate

100%
80%

60%
40%
Iron Ore

20%
0%

0

100000200000300000
Ship Size (DWT)

400000500000Fig. 4.22 Distribution of Draught Rate (Brazil~China)82

4.7 Data Findings of MLDB
Based on the operational data extracted from MLDB, it can be seen that theavailability of ship operational data especially (draft) is important for cargo volume
estimation results. Lack of ship operational data might have caused the estimated volume
different from the trade data volume from UN Comtrade. The detailed explanation is
described as follows:
4.7.1 Findings of Data Availability
To check the availability of ship operational data, the selected country is divided into
two categories: (1) Developed Country i.e. Japan, Australia, China, Korea, and America;(2) Developing Country i.e. Malaysia, India, Vietnam, Ghana, and Indonesia. The
availability of each category is shown as follows:(1) Developed Country
700

Japan - Oita Port

600

500
400
300
200
100
0
Amount of ship
Amount of draft

2010
195
0

2011
534
187

2012
519
101

2013
618
610

2014
553
553

2015
646
646

2016
523
523

2017
480
480

Fig. 4.23 Data Availability of Oita Port
2500

Australia - Newcastle Port
2000

1500
1000
500
0
Amount of Ships
Amount of draft

2010
1445
0

2011
2023
106

2012
1914
81

2013
1785
1770

2014
1850
1850

2015
1908
1908

2016
1947
1947

2017
1971
1971

Fig. 4.24 Data Availability of Newcastle Port

83

600

Korea - Busan Port

500
400

300
200
100
0
Amount of ships
Amount of draft

2010
369
0

2011
471
57

2012
522
103

2013
506
503

2014
396
396

2015
448
448

2016
518
518

2017
513
513

Fig. 4.25 Data Availability of Busan Port

1200

America - HoustonPort

1000
800
600
400
200
0
Amount of ships
Amount of draft

2010
674
0

2011
869
46

2012
821
51

2013
893
885

2014
999
999

2015
959
959

2016
909
909

2017
908
908

Fig. 4.26 Data Availability of Houston Port

2500

China - Qingdao Port

2000
1500
1000
500
0
Amount of Ships
Amount of draft

2010
1908
0

2011
2125
110

2012
2180
181

2013
1809
1774

2014
1578
1578

2015
1588
1588

2016
1538
1538

2017
1728
1728

Fig. 4.27 Data Availability of Qingdao Port

84

Table 4.15 Availability Data of Developed Country
PeriodJapan

Australia

China

Korea

America

Oita

Newcastle

Qingdao

Busan

Houston∑Ships∑draft∑Ships∑draft∑Ships∑draft∑Ships∑draft∑Ships∑draft

2010

195

0

1445

0

1908

0

369

0

674

0

2011

534

187

2023

106

2125

110

471

57

869

46

2012

519

101

1914

81

2180

181

522

103

821

51

2013

618

610

1785

1770

1809

1774

506

503

893

885

2014

553

553

1850

1850

1578

1578

396

396

999

999

2015

646

646

1908

1908

1588

1588

448

448

959

959

2016

523

523

1947

1947

1538

1538

518

518

909

909

2017

480

480

1971

1971

1728

1728

513

513

908

908

(2) Developing Country
Malaysia - Lumut Port

300
250

200
150
100

50
0

Amount of Ships
Amount of draft

2010
10
0

2011
224
46

2012
191
66

2013
161
160

2014
185
185

2015
256
256

2016
278
278

2017
226
226

Fig. 4.28 Data Availability of Lumut Port
700

India - Mumbai Port

600

500
400
300
200
100
0
Amount of ships
Amount of draft

2010
102
0

2011
189
22

2012
259
50

2013
208
206

2014
403
403

2015
460
460

2016
455
455

2017
579
579

Fig. 4.29 Data Availability of Mumbai Port
85

800

Vietnam - Saigon Port

700
600
500
400
300
200
100
0
Amount of ships
Amount of draft

2010
666
0

2011
644
44

2012
674
67

2013
616
612

2014
578
578

2015
560
560

2016
591
591

2017
529
529

Fig. 4.30 Data Availability of Saigon Port

250

Ghana - Takoradi Port

200
150
100

50
0
Amount of ships
Amount of draft

2010
57
0

2011
185
3

2012
200
53

2013
150
150

2014
115
115

2015
108
108

2016
128
128

2017
169
169

Fig. 4.31 Data Availability of Takoradi Port
Indonesia - Tanjung Priok Port

400
350
300
250

200
150
100

50
0
Amount of ships
Amount of draft

2010
247
0

2011
375
45

2012
346
68

2013
326
325

2014
344
344

2015
331
331

2016
370
370

2017
267
267

Fig. 4.32 Data Availability of Tanjung Priok Port86

Table 4.16 Availability Data of Developing Country
PeriodMalaysia

India

Vietnam

Ghana

Indonesia

Lumut

Mumbai

SaigonTakoradiTanjung Priok∑Ships∑draft∑Ships∑draft∑Ships∑draft∑Ships∑draft∑Ships∑draft

2010

10

0

102

0

666

0

57

0

247

0

2011

224

46

189

22

644

44

185

3

375

45

2012

191

66

259

50

674

67

200

53

346

68

2013

161

160

208

206

616

612

150

150

326

325

2014

185

185

403

403

578

578

115

115

344

344

2015

256

256

460

460

560

560

108

108

331

331

2016

278

278

455

455

591

591

128

128

370

370

2017

226

226

579

579

529

529

169

169

267

267

Based on the results of the finding, we can conclude that based on the number ofarrival ships, the data are reliable from 2013 onward both developed countries anddeveloping countries. However, based on the availability of draft, the data is reliable from2014 onward, which is the number of ships and the amount of draft are the same for bothdeveloped countries and developing countries.
4.7.2 Findings of Cargo Estimation Results
Below are the estimation cargo results of a developed country and developing
country shown as follows:(1) Developed Country
100000000

140000000

14000000

120000000

12000000

100000000

10000000

90000000

60000000
50000000
40000000
30000000

Cargo Volume (t)70000000

Cargo Volume (t)Cargo Volume (t)80000000

80000000

8000000

60000000

6000000

40000000

4000000

20000000

2000000

20000000
10000000

0100% Cargo
Modified
Draught
Trade Data

Iron Ore
86507324

81256528
82884716

0100% Cargo
Modified
Draught
Trade Data

Coal
114931756
107322876
119749649

0100% Cargo
Modified
Draught
Trade Data

Grain & Others
11534953
11141285
12351717

Fig. 4.33 Cargo Estimation Results (Australia~Japan)
8760000000

60000000

50000000

50000000

40000000

40000000

8000000
7000000

30000000

Cargo Volume (t)Cargo Volume (t)Cargo Volume (t)6000000

30000000

20000000

20000000

10000000

10000000

5000000
4000000
3000000
2000000
1000000

0100% Cargo
Modified
Draught
Trade Data

Iron Ore
50758246
49726808
50949931

0

Coal49894285

100% Cargo
Modified
Draught
Trade Data

47065859
54928325

0100% Cargo
Modified Cargo
Trade Data

Grain & Others
601747157043116719127

Fig. 4.34 Cargo Estimation Results (Australia~Korea)

600000000

90000000

50000000

80000000

45000000

500000000

40000000

300000000

200000000

100000000

35000000

60000000

Cargo Volume (t)400000000

Cargo Volume (t)Cargo Volume (t)70000000

50000000
400000000Iron Ore
491943119
475288948
548354530

25000000
20000000

30000000

15000000

20000000

10000000
5000000

10000000

100% Cargo
Modified
Draught
Trade Data

30000000

0100% Cargo
Modified Cargo
Trade Data0
Coal78141995
74804274
79595444

100% Cargo
Modified Cargo
Trade Data

Grain &
Others43075896
39706038
46754508

Fig. 4.35 Cargo Estimation Results (Australia~China)
The findings of country trade coverage are shown in Fig. 4.33 ~ Fig. 4.35. In the caseof Australia to Japan, Australia to Korea, and Australia to China, the cargo estimation is
fulfilling the same amount as statistics trade data. Most of them reaching more than 90%
coverage of cargo.

88

(2) Developing Country
Indonesia~Korea

35000000

35000000

30000000

30000000

25000000
20000000

20000000
15000000

10000000

10000000

5000000

5000000

035579266

30000000

10000000

100 Cargo
Modified
Draught
Trade Data

28437728,34

40000000

200000000Coal31872992

100% Cargo
Modified
Draught
Trade Data

50000000

25000000

15000000

Indonesia ~ China
60000000

Cargo Volume (t)40000000

Cargo Volume (t)Cargo Volume (t)Indonesia~Japan
400000000Coal31340558

100% Cargo
Modified
Draught
Trade Data

30348972,63
35574104

Coal
40454907
37513681,1249781974

Fig. 4.36 Cargo Estimation Results (Indonesia: Japan, Korea, China)

Vietname~Korea

Vietname~Japan

4500000

4000000

1200000

3500000

6000005000004000003000001000000

Cargo Volume (t)700000Cargo Volume (t)Cargo Volume (t)8000008000006000002500000
2000000

1000000
2000005000001000000100% Cargo
Modified Draft
Trade Data

3000000

1500000

4000002000000Vietname ~ China

1400000

900000Coal
599077579366840561

100% Cargo
Modified
Draught
Trade Data

Coal
94119580041813231170100% Cargo
Modified
Draught
Trade Data

Coal
2983066
26986594139131

Fig. 4.37 Cargo Estimation Results (Vietnam: Japan, Korea, China)
The findings of country trade coverage are shown in Fig. 4.36 ~ Fig. 4.37. In the caseof Indonesia to Japan-Korea-China, and Vietnam to Japan-Korea-China, the cargoestimation is not fulfilling the same amount as statistics trade data. Most of them arebelow 90 % coverage of cargo. It indicates AIS data and operation data may be missing
in some countries, especially from developing countries like Indonesia and Vietnam.
89

Chapter 5
Shipper Model
As discussed in Section 3.2, the ship allocation model is composed of shipper, shipowner,and operator models. The data extracted from the MLDB were used to develop thesemodels. Details of the shipper model are discussed in this section, and the other two
sections will be discussed in the next section. The target of the development ship
allocation model in this study are:

Data used: All of the operation data in 2014

Country: Australia to Japan

Ship Type: Bulk Carrier

Cargo Type : Iron Ore

5.1 Development of Shipper Model
5.1.1 Overview of Shipper Model
The shipper model issues a request for cargo transportation between two or more
ports from Australia to Japan. Herein, the shipper model was generated using cluster
analysis, which is a method of defining similarities in data, grouping similar items, andclassifying them into clusters. Using hierarchical cluster analysis, we clustered shippers
between Japan and Australia.

90

EXPORTER

IMPORTER

A

C

B

D

Location of Nippon Steel
Sumikin harbor
Australia's iron ore
loading porto Cargo type is Iron ore
o Cargo Volume 5 x 106 ton

Australia

Fig. 5.1 Illustration of Shipper Model
5.1.2 Method of Shipper ModelIn this study, the shipper model is generated by using the following steps:
A. Extracting Data from MLDB
Operation data from 2014 from Australia to Japan were extracted from the MLDB.
The information extracted from the MLDB included operation, port (origin anddestination), and ship (name, principal particulars, etc.) data. By utilizing these data, weeasily analyzed the number of port callings from Australia to Japan.
Table 5.1 Example Data of Ship Operation from MLDB
ArrivalVesselVesselVesselArrivalArrivalMax

Previous Port

Port

Name

Type

IMO

Date

Draught

Draught

Departure Dtae

Kashima

SHIP BBC

9355147

18

18,105

Kashima

SHIP BBC

9355147

17,2

18,105

Kashima

SHIP BBC

9355147

18

18,105

Kawasaki

SHIP BBC

9355147

18,1

18,105

Kisarazu

SHIP BBC

9355147

18

18,105

2015-12-02
01:55:46
2014-03-07
23:10:20
2013-01-31
07:20:31
2017-08-04
05:41:07
2016-01-12
07:56:03

2015-11-17
05:26:02
2014-02-18
02:10:20
2013-01-17
05:42:29
2017-07-22
13:23:11
2015-12-30
02:57:05

91

B. Define a Shipper Using Cluster Analysis
Data clustering is the process of identifying natural groupings or clusters within
multidimensional data based on some similarity measure (e.g. Euclidean distance) [128130]. It is an important process in pattern recognition and machine learning [131] [132].
Furthermore, data clustering is a central process in Artificial Intelligence (AI) [133].
Clustering algorithms are used in many applications, such as image segmentation [134136], vector and color image quantization [137-139], data mining [140], compression[141], machine learning [142], etc. A cluster is usually identified by a cluster center (orcentroid) [143]. Data clustering is a difficult problem in unsupervised pattern recognition
as the clusters in data may have different shapes and sizes [145].
Clustering analysis divides data into groups (clusters) that are meaningful, useful, prboth. If meaningful groups are the goal, then the clusters should capture the natural
structure of the data. In some cases, however, cluster analysis is only a useful starting
point for other purposes such as data summarization. Whether for the understanding ofutility, cluster analysis has long played an important role in a wide variety of fields:
psychology and other social sciences, biology, statistics, pattern recognition, information
retrieval, machine learning, and data mining.
There are three important techniques in cluster analysis i.e. K-means, Agglomerative
Hierarchical Clustering. And DBSCAN. In this study, the Agglomerative Hierarchical
Clustering concept is used. The approach of this concept refers to a collection of closely
related clustering techniques that produce a hierarchical clustering by starting with eachpoint as a singleton cluster and then repeatedly merging the two closest clusters until asingle, all-encompassing cluster remains. Some of these techniques have a naturalinterpretation in terms of graph-based clustering, while others have an interpretation interms of a prototype-based approach.

92

Agglomeration hierarchical clustering techniques are by far the most common andoften displayed graphically using a three-like diagram called a dendrogram, whichdisplays both the cluster-subcluster relationship and the order in which the clusters weremerged (agglomerative view) or split (divisive view). Below is sample data to illustrate
the behavior of the various hierarchical clustering algorithms i.e. sample data that consistsof 6 two-dimensional points, which are shown in Fig. 5.2. The x and y coordinates of thepoints and the Euclidean distances between them are shown in Tables 5.2 and 5.3,
respectively.

Fig. 5.2 Set of 6 two-dimensional points, [145]
Table. 5.2 x y coordinates of 6 points

93

Table. 5.3 Euclidean distance of 6 points

The hierarchical clustering result of the 6 points using several types i.e. Single linkage,Complete linkage, Group average linkage, and Ward’s shown as follows:

Fig. 5.3 Single link clustering of 6 two-dimensional points, [145]Fig. 5.3 shows the result of applying the single linkage link technique to the example data
set of six points above. Fig. 5.3 (a) shows the nested clusters as a sequence of nested
ellipses, where the numbers associated with the ellipses indicated the order of theclustering. Fig. 5.3 (b) shows the same information but as a dendrogram. The height at
which two clusters are merged in the dendrogram reflects the distances of the two clusters.
94

Fig. 5.4 Complete link clustering of 6 two-dimensional points, [145]Fig. 5.4 shows the result of applying the complete linkage link technique to the example
data set of six points above. Fig. 5.4 (a) shows the nested clusters as a sequence of nested
ellipses, where the numbers associated with the ellipses indicated the order of theclustering. Fig. 5.4 (b) shows the same information but as a dendrogram. The height at
which two clusters are merged in the dendrogram reflects the distances of the two clusters.

Fig. 5.5 Group average clustering of 6 two-dimensional points, [145]95Fig. 5.5 shows the result of applying the group average clustering technique to theexample data set of six points above. Fig. 5.5 (a) shows the nested clusters as a sequenceof nested ellipses, where the numbers associated with the ellipses indicated the order ofthe clustering. Fig. 5.5 (b) shows the same information but as a dendrogram. The heightat which two clusters are merged in the dendrogram reflects the distances of the twoclusters.

Fig. 5.6 Ward’s clustering of 6 two-dimensional points, [145]In this study, to define a shipper between Australia and Japan, we identified thenumber of port callings in 2014 using cluster analysis. Where the clustering process canbe described as follows:(1) Calculate the number of port callings
The number of port callings was calculated by identifying data extracted from the
MLDB. By using a matrix between the ports (P1, P2, …, Pn) and ships (S1, S2, …, Sn).
As shown in Table 5.4 (1), the number of port callings could be calculated.
(2) Measure the Euclidean distance
Euclidean distance is a measure of the true straight-line distance between two points
in Euclidean space. In hierarchical clustering, in which the distance measure is Euclidean,
96

data must first be normalized or standardized to prevent the covariant with the highest
variance from driving the clustering. The data consist of many calling ships whose
weights and numbers of calls differ. Therefore, it was necessary to standardize thedifferences in each property. Data standardization was performed as shown in Table 5.4(2) for each port. Then, the Euclidean distance was calculated using Eq. (3), the result of
which is shown in Table 5.4 (3). Where xi and yi are the numbers of calls after
standardizing ship i at ports x and y, respectively.

(3)

Table. 5.4 Cluster analysis process(1) Port Calling Calculation

Ship
S1
Port
P1
0
P2
0
P3
0
P4
5
P5
2
(2) Standardization
Ship
Port
P1
P2
P3
P4
P5

S2

S3

S4

S5

S6

0
0
0
1
0

1
1
0
0
0

0
0
1
0
0

2
3
0
0
0

0
2
0
0
0

S1

S2

S3

S4

S5

S6

-0.7
-0.9
-0.4
2.19
2.24

-0.7
-0.9
-0.4
0
-0.4

0.65
0
-0.4
-0.5
-0.4

-0.7
-0.9
2.24
-0.5
-0.4

1.96
1.73
-0.4
-0.5
-0.4

-0.7
0.87
-0.4
-0.5
-0.4

(3) Calculation of Euclidean distances
Port
Port
P1
P2
P3
P4
P5

P1
1.71
3.94
4.04
3.94

P2

P3

P4

P5

1.71

3.94
4.08

4.04
4.21
3.87

3.94
4.08
3.79
0.49

4.08
4.21
4.08

3.87
3.79

0.49

97

(3) Clustering using hierarchical cluster analysis
First, before any clustering was performed, it was necessary to populate a proximity
matrix with the distance between each point using a distance function. Then, the matrix
was updated to display the distance between each cluster. In this study, to measure thedistance between two clusters, we applied the average linkage method, which iscommonly used and represents a natural compromise between linkage measures toprovide a more accurate evaluation of the distance between clusters [145]. The distance
between two clusters is calculated using Eq. (4).

,(4)

where Cn is a cluster, xn is a port, and d(C1, C2) is the distance between cluster C1 and C2.
The goal of this method was to group heterogeneous port data into homogeneousclusters. By doing so, we could identify groups without previous knowledge of groupmembership or even the number of possible groups. Thus, shippers operating between
Australia and Japan could easily be defined. Hierarchical cluster analysis is best
illustrated using a dendrogram (a visual display of the clustering process).

5.2 Result of Shipper Model
The shipper model in this study is defined based on the exporter and Consignee's
point of view. The results of shippers operating between Australia and Japan (2014)
illustrated using a dendrogram shown in Fig. 5.7 (Importer point of view) and Fig. 5.9(Eksporter point of view). Fig. 5.7 shows the results of shipper clusters based on theConsignee’s point of view. Based on the Consignee’s point of view, the ports weregrouped into four clusters (Shippers A–D), defined as follows:Shipper A (Kawasaki, Mizushima, Chiba, and Fukuyama)Shipper B (Oita, Kashima, and Kisarazu)



Shipper C (Nagoya, Wakayama, and Tobata)Shipper D (Higashi-Harima, Himeji, Kure, Saganoseki, Tomakomai, andHachinohe)98

Tobata

Wakayama

NagoyaC

Kisarazu

Oita

KashimaB

Higashi-Harima

Kure

Saganoseki

Tomakomai

Hachinohe

Mizushima

Fukuyama

Kawasaki

Chiba

HimejiD

A

Fig. 5.7 Shippers Cluster Based on Consignee’s Point of View
Table. 5.5 Amount of Iron Ore Imported from Australia (2014)
Cluster

A

B

C

D

Port Name

Amountof Ship

Amountof Cargo

DWT

L (m)

B (m)

d (m)

Chiba

38

6,736,992

220,000

300

50

18

Fukuyama

47

8,843,141

220,000

300

50

18

Kawasaki

19

3,829,906

260,000

340

50

18

Mizushima

47

9,387,567

220,000

340

50

18

Kashima

56

11,203,358

300,000

340

60

19

Oita

80

15,847,591

400,000

450

60

25

Kisarazu

52

11,680,407

300,000

330

60

19

Tobata3

311,937

160,000

327

43

16

Wakayama

11

1,056,076

160,000

300

43

14

Nagoya14

1,439,954

110,000

300

43

16

Kure

19

3,124,094

276,000

360

45

18

Himeji8

240,329

257,000

335

47

16

6,199,892

180,000

330

47

17

Higashi-Harima

4099

By using the clustering analysis, we can easily calculate the amount of cargo (ironore) imported from Australia in 2014 as shown in Table 5.5. Moreover, we can easily
identify the correlation of port sizes, amount of cargo, and clusters as shown in Fig. 5.8.

Fig. 5.8 Correlation of port based on the Consignee’s point of view
However, the results of shipper clusters based on the Shipper’s point of view are
shown in Fig. 5.9. Based on the Shippers’s point of view, the ports were grouped into
three clusters (Shippers A–C), defined as follows:Shipper A (Port Hedland)

Shipper B (Dampier, Port Walcott)Shipper C (Experance, and Parker Point)

Not only importer point of view, but by using the clustering analysis we also can easily
calculate the amount of cargo (iron ore) exported to Japan from Australia in 2014 asshown in Table 5.6. Moreover, we also can easily identify the correlation of port sizes,
amount of cargo, and clusters as shown in Fig. 5.10.

100

Parker Point

Esperance

C

Port Walcott

B

Dampier

Port Hedland

A

Fig. 5.9 Shippers Cluster Based on Shipper’s Point of View

Fig. 5.10 Correlation of port based on the Shipper’s point of view101Table. 5.6 Amount of Iron Ore Exported to Japan (2014)
Cluster

Port NameA

Port Hedland

150

DampierB

C

Amount of Ship Amount of Cargo

DWT

L (m)

B (m)

d (m)

28 MT

340,000

335

60

19.5

30

6 MT

260,000

330

55

19.2

Port Walcott

200

38 MT

250,000

340

55

19.5

Esperance

13

1.9 MT

220,000

300

50

18.5

Parker Point

21

3.7 MT

220,000

300

50

18

As shown in Fig. 5.7 and Fig. 5.9, the destination ports (Japan) and the origin ports(Australia) can be identified clearly. As a result, the shippers from Australia to Japan weregenerated as in Table 5.7.
Table. 5.7 Shippers from Australia to Japan, 2014
ClusterA

Origin Port (Consignee)

Destination Port (Shipper)

Dampier

Chiba

Parker Point

Fukuyama

Port Hedland

Mizushima

Port Walcott

KawasakiEsperance

B

Dampier

Kashima

Parker Point

Kisarazu

Port Hedland

Oita

Port Walcott
Esperance
NagoyaC

Port Walcott

TobataWakayama

D

Port Hedland

Higashi-Harima

Port Walcott

Kure
Himeji102

5.3 Evaluation of Shipper Model
As mentioned in the previous section, ports were grouped into 4 clusters based on
the Consignee’s point of view, and based on Shipper’s point of viewports were grouped
into 3 clusters. We confirmed the cluster analysis result based on the following points:
5.3.1 Comparison with actual locations
Based on Fig. 5.11, it can be seen that based on the Consignee point of view the
Cluster A (Chiba, Kawasaki, Fukuyama, and Mizushima) matched with the location of
JFE Steel. Cluster B (Kashima, Oita, and Kisarazu) and Cluster C (Nagoya, Wakayama,
and Tobata) are matched with the Nippon Steel & Sumitomo Metal Terminal (NSSMT),
and the last Cluster D (Kure, Himeji, and Higashi-Harima) are matched with the locationof Kobelco Nisshin. From the result can be concluded that based on the Consignee pointof view, the cluster analysis is matched with the current location of the company.

TobataWakayama

NagoyaC

Kisarazu

Oita

KashimaB

Higashi-Harima

Kure

Saganoseki

Tomakomai

Hachinohe

Mizushima

Fukuyama

Kawasaki

Chiba

JFE Steel's location is match

HimejiD

A

Nippon Steel & Sumitomo Metal Terminal (NSSMT)

Fig. 5.11 Matching location of shipper based on Consignee point of view103

In the case of the results of Shipper point of view based on Fig. 5.12, Cluster A (PortHedland) is matched with the BHP Billington and Atlas Iron. Cluster B (Dampier, andPort Walcott) and Cluster C (Experance and Parker Point) are matched with Rio Tinto
company locations. This shipper is also divided into two clusters because the port
constraints in Cluster B and C are quite different.

From the result can be concluded thatbased on the Shipper’s point of view, the cluster analysis is matched with the current
location of the company.

B

Esperance
M

L

BHP Biliton
FMG
Atlas Iron

Parker PointCPort Walcott

Dampier

Port Hedland

A

Rio Tinto

Fig. 5.12 Matching location of shipper based on Exporter point of view
5.3.2 Consistency of the cluster results
In this study, to confirm the consistency of the cluster, the cluster results from 2014
(Australia~Japan) are compared with the cluster results from 2017 (Australia~Japan).
The comparison results are shown in Fig. 5.13 and Fig. 5.14 as follows:

104

2017
TobataWakayama

NagoyaB

TobataB

Wakayama

NagoyaKisarazu

Oita

KashimaD

Kisarazu

D

Oita

Himeji

Kure

Saganoseki

Tomakomai

Hachinohe

Mizushima

Fukuyama

Kawasaki

Chiba

Higashi-Harima

2014

Kashima

Muroran

Saganoseki

A

Kure

Higashi-Harima

Kawasaki

Fukuyama

Mizushima

Chiba

A
C

C

Fig. 5.13 Consistency of the cluster of 2014 and 2017 based on

Consignee’s point of view

105

2014

2017

Fig. 5.14 Consistency of the cluster of 2014 and 2017 based on
Consignee’s point of view106

Based on Fig. 5.13 and Fig.5.14, it can be concluding that the shipper from 2014 andthe shipper from 2017 have remained the same. It means that the cluster results are
consistent with the current situation.
5.3.3 Comparison with ship locations
Based on the result in Table 5.7, most ships operating from Australia to Japan loadedcargo from two or more ports in Australia and unloaded at two or more ports in Japan.
Here, we compare the results of cluster analysis with actual operation. Some typicaloperations are shown in Table 6.1, where the gray represents ports in Australia, and thewhite represents ports in Japan.
In the case of Ship A, the cargo was loaded at Port Walcott–Port Hedland thenunloaded at Mizushima, Chiba, and Fukuyama, which matches the operation of Shipper
A. In contrast, in the case of Ship C, the cargo was loaded at Port Walcott, then unloaded
at Kisarazu, Kashima, and Oita, matching Shipper B. As discussed in this section, cluster
analysis matched actual location and ship operation conditions.
Table 5.8 Characteristics of actual ship operation
Ship A

Ship C

OriginDestination

OriginDestination

Port Walcott

Fukuyama

Port Walcott

Kisarazu

Fukuyama

Mizushima

Kisarazu

Port Walcott

Mizushima

Port Hedland

Port Walcott

Kashima

Port Hedland

Mizushima

Kashima

Port Walcott

Mizushima

Port Hedland

Port Walcott

Oita

Port Hedland

Chiba

Oita

Port Walcott

Chiba

Mizushima

Port Walcott

Kisarazu

Mizushima

Port Hedland

Kisarazu

Port Walcott

Port Hedland

Chiba

Port Walcott

Kashima

Chiba

Fukuyama

Kashima

Port Walcott

1075.4 Findings of Shipper Model(1) Characteristics of Shipper Model Based on Owned Company Location
The characteristics of the shipper model based on the importer point of view are
shown in Table 5.9, and the the characteristics of shipper model based on the exporter
point of view are shown in Table 5.10.
Table 5.9 Characteristic of shipper model based on Consignee’s point of view
Port Name

DWT

L (m)

B (m)

d (m)

Cargo (MT)Chiba

220,000

300

50

18

6,7

Fukuyama

220,000

300

50

18

8,8

Kawasaki

260,000

340

50

18

3,8

Mizushima

220,000

340

50

18

9,4

Nippon Steel &Sumitomo Metal
Terminal (NSSMT)

Kashima

300,000

340

60

19

11,2

Oita

400,000

450

60

25

15,8

Kisarazu

300,000

330

60

19

11,7

Nippon Steel &Sumitomo Metal
Terminal (NSSMT)

Tobata160,000

327

43

16

0,3

Wakayama

160,000

300

43

14

1,1

Nagoya110,000

300

43

16

1,4

Kure

276,000

360

45

18

3,1

Himeji

257,000

335

47

16

0.2

Higashi-Harima

180,000

330

47

17

6,1

Cluster

JFE Steel

KOBELCO
Nisshin

NSSMT
NSSMT
Nisshin SteelNSSMT KOBELCONSSMTJFE SteelJFE SteelNSSMTJFE SteelNSSMTJFE SteelFig. 5.15 Characteristics of shipper model (based on Consignee’s point of view)
108

Table 5.10 Characteristic of shipper model based on Shipper’s point of view
Cluster

Port Name

DWT

L (m)

B (m)

d (m)

Cargo (MT)BHP Biliton

Port Hedland

340,000

335

60

19.5

28

Dampier

260,000

330

55

19.2

6

Port Walcott

250,000

340

55

19.5

38

Esperance

220,000

300

50

18.5

1.9

Parker Point

220,000

300

50

18

3.7

RIO Tinto A

Rio Tinto B

BHP Biliton

Parker Point
Dampier
Port Walcott

Port Hedland

Rio Tinto

2 MT
10 MT

Esperance
30 MT
40 MT

Fig. 5.16 Characteristics of shipper model based on Shipper’s point of view
Based on the information above it can be concluded that based on the importer point ofview the characteristics of shipper from Japan is defined as follows:

JFE Steel used a Medium (M) size of the ship in their operationNSSMT is divided into two: Very Large (VL) and small ship (S)Kobelco Nisshin used the Large (L) ship size due to the port constraint.

However, based on the exporter point of view the characteristics of shipper from Australia
is defined as follows:

BHP Billiton used a Very Large (VL) size of the ship in their operation



Rio Tinto A used Large (L) ship size in their operation



Rio Tinto B used the Medium (M) ship size due to the port constraint.
109

(2) Characteristics of Shipper Model Based on Port Restrictions
The characteristics of the shipper model based on the port restrictions between
Australia~Japan based on Table 5.11 is shown as follows:Port Hedland can have covered all of the operations since the port constraints
are very large.

The constraint of cluster C on the Japan side is the most severe.

In the case of the very large ships, the need for the cargo is covered by the port
Hedland.



Mostly, the need for the Japan cluster is covered by cluster B and cluster C fromthe Australian side.
Table 5.11 Characteristic of shipper model based on port restriction

Port NameDWTPort Hedland 340,000
Dampier

260,000

Port Walcott 250,000

Esperance

220,000

Parker Point 220,000

Port Name

DWT

Chiba

220,000

Fukuyama

220,000

Kawasaki

260,000

11%

Mizushima

220,000

22%

Kashima

300,000

Oita

400,000

Kisarazu

300,000

Tobata160,000

Wakayama

160,000

Nagoya110,000

Kure

276,000

Higashi-Harima

180,000

Cluster

Cluster
51%

A

A

38%
B
69%

C

9%
41%

B

100%
C

40%
19%

D

(3) Characteristics of Shipper Model Based on the Relationships between Shippers
The characteristics of the shipper model based on the port restrictions between
Australia~Japan based on Table 5.12 is shown as follows:

JFE Steel has a strong relationship with BHP Billiton since the coverage of the
cargo volume is the highest. Followed by the Rio Tinto in 2nd place.
110



NSSMT has a very strong relationship with Rio Tinto since the coverage isreaching 78%.

Table 5.12 Characteristic of shipper model based on the relation between shippers
Port Name

DWT

Cluster

BHP Biliton

340,000

A

Rio Tinto

Rio Tinto

260,000
250,000
220,000
220,000

Cluster

Port Name

220,000

51%
A

38%

B

JFE Steel

11%
69%

C
41%

B

100%
40%
19%

220,000
260,000
220,000

22%

9%

DWT

C

D

Nippon Steel &Sumitomo Metal
Terminal(NSSMT)

300,000

Nippon Steel &Sumitomo Metal
Terminal(NSSMT)

160,000

Nisshin Steel

276,000

KOBELCO

180,000

400,000
300,000
160,000
110,000

NSSMT
NSSMT

NSSMT

Nisshin SteelKOBELCONSSMTJFE SteelJFE SteelNSSMTJFE SteelNSSMTJFE Steel

BHP Biliton
Rio Tinto
Rio Tinto
Rio Tinto

2 MT

10 MT

Rio Tinto
30 MT
40 MT

Fig. 5.17 Characteristics of shipper based on the relationship between shippers
111

Chapter 6
Shipowner Model
6.1 Development of Shipowner Model
The shipowner model can be used to estimate shipment days, cargo amounts, andshipment costs in response to a transportation request from an operator. To realize thismodel, we generated the draft rate, average speed in loading and ballast conditions, andtime in port due to loading and ballast conditions using deep learning on data extracted
from the MLDB.
6.1.1 Method of Shipowner Model Using Deep Learning
In this fast-growing digital world, Big Data and Deep learning are the high attentionof data science. Big Data is the collection of a huge amount of digital raw data that isdifficult to manage and analyze using traditional tools. As digital data is growing
exponentially in different shapes, formats and sizes, therefore it is very important tomanage this large volume of data according to the needs of the organization.
Deep learning methods are extensively applied to various fields of science andengineering such as speech recognition, image classifications, and learning methods inlanguage processing. Similarly, traditional data processing techniques have several
limitations of processing a large amount of data. In addition, Big Data analytics requires
new and sophisticated algorithms based on machine and deep learning techniques toprocess data in real-time with high accuracy and efficiency.
112

Deep learning is an expressive machine learning technique that has recently attracted
considerable attention. Machine learning is a mechanism for inputting training data intoa learning machine, generating a learning model, and processing data using the learned
model. The key benefit of deep learning is the analysis of massive amounts ofunsupervised data, making it a valuable tool in big data analytics [146].
Deep Learning is a big data analytics tool. The advantage of deep learning is its potential
to serve as the solution to data analysis and learning problems at a large rate of input data.
Deep learning is capable to automatically obtain insights from complex unlabeled andunsupervised data. Deep learning algorithms are an assorted layer. Each layer comprises anonlinear transformation of its input and produces an interpretation in its output. Deeplearning aims to perform the learning of complicated and abstract insights from the input data.
In a hierarchical architecture, deep learning passing the data through multiple transformation
layers.

As shown in Fig 6.1, the first layer of a neural network used for deep learning is the
input layer. Each node in this layer takes an input and passes its output as input to eachnode in the next (hidden) layer, which has no connection to the outside and is only
activated by nodes in the previous layer.
Hidden layers

Model test

Output layer
Training
Data

Input
layer

Predicted
data
compared to

Test
Data

Validation
Data

Learning
Model

Fig. 6.1 Structure of a deep learning neural network113

As shown in Fig 6.1, the deep learning architecture consists of an input layer,
hidden layer, and output layer, handles the training data validation data, test data,
and results from a learning model [25]. Each item will be described as follow.
a.

The input layer is where the input data is received by the model. The input data
(vectors) is divided into assorted nodes that are featured as input.

b.

Hidden layers comprise one or more layer bridges input layer and output layer.
The hidden layer aims the realization to model non-linear functions as it makes
various pattern recognition possible with a layered activation function.

c.

Output layer gives a prediction of real value (regression) illustrated in orprobability (classification) depend on the model setupd.

The connection between layers is a feed-forward network that connects thenodes between layers. A connection is the representation of variable flow
between nodes.

e.

Training data is dataset comprises the example of the model parameter.

f.

Validation data is a dataset used to measure the responses of learned training
data. Overfitting training can be detected as the error of validation data keepincrease compared to the training data error.

g.

Test data is the dataset used to evaluate the final trained model. Generalizationof the trained model can be assessed with comparison to test data that has neverbeen used in the training process.

6.1.2 Deep Learning in This StudyIn this study, draft rate, average voyage speed, and time in port were predicted using
Deep Learning (DL) based on the following steps:(1) Collect training data
Usually, neural networks are trained to perform single-step prediction, in which thepredictor uses some available input and outputs observations to estimate a variable ofinterest for the timestep immediately following the latest observation. In this study, allshipping data were extracted from the MLDB. Around 75% of ship operation data from
Australia to Japan in 2014 were used for training data and the remaining 25% were used
for evaluation.
114

(2) Generate learning model
To generate a learning model, the input layer, output layer, and hyperparameters
for each learning model which is consist of three distinct learning models: draught
rate, average voyage speed, and port staying in time were set as follows:a. Draught rate learning model
Ship operation data of bulk carriers operating from Australia to Japan from2013 until 2015 is extracted from MLDB. While the only arrival in Japan ports
operation that will be used, not all data can be utilized due to error or abnormal
recorded draught data. The input and output layers are set as shown in Table 6.1
below.
Table 6.1 DL Input and Output Layer in Draught Rate Learning Model
Deep Learning Input Layer
Data Type

Deep Learning Output Layer

Item

Data Type

Item

DWT

Ship

Draught Rate (%)

Length (L)Breadth (B)Depth (d)Max Cargo (m3)
Ship Design Speed (kt)Ship

M/e Power (kW)
Built Year
IMO

Owner
Operator
Flag
Classification
Built Country
Arr. Port Max. DWTDep. Port Max DWT
Port

Arr. Port Max L, B, d (m)Dep. Port Max L, B, d (m)Dep. Port -Arr. Port Name

Route

Dep. Port – Arr. Port DistancesTrain Target

Ship Draught Rate (%)

115

b. Average voyage speed learning model
In the following step from the draught rate learning model, the data used isevaluated again focusing on average voyage speed and resulted from sailing days.
The sailing days needed for the ship operating from Australia to Japan can beunderstood by the recorded departure date from Australia and recorded arrival
date in Japan. The actual sailing days of the ship will be compared with theestimated sailing days with the ship design speed which is expected to be the speed
at which the ship operates. Based on the selected data, the arrangement of items
for the input layer and output layer is shown below in Table 6.2.
Table 6.2 DL Input and Output Layer in Average Voyage Speed Learning
Model
Deep Learning Input Layer
Data Type

Deep Learning Output Layer

Item

Data Type

Item

DWT

Ship

Average Voyage
Speed (kt)

Length (L)Breadth (B)Depth (d)Max Cargo (m3)
Ship Design Speed (kt)Ship

M/e Power (kW)

Built Year
IMO
Owner
Operator
Flag
Classification
Built Country
Arr. Port Max. DWTDep. Port Max DWT

Port

Arr. Port Max L, B, d (m)Dep. Port Max L, B, d (m)Dep. Port -Arr. Port Name
Route

Dep. Port – Arr. Port Distances

Predicted

Ship Draught Rate (%)

Train Target

Average Voyage Speed (kt)116

c. Port staying time learning model
The later stage from the average voyage speed learning model is to evaluate port
staying time spent by ship in Japan port. The port staying time needed for the ship
operating from Australia to Japan can be drawn by the recorded arrival date inJapan and recorded departure date from Japan. The arrangement of items for the
input layer and output layer is shown below in Table 6.3.
Table 6.3 DL Input and Output Layer in Port Staying Time Learning Model
Deep Learning Input Layer
Data Type

Deep Learning Output Layer

Item

Data Type

Item

DWT

Ship

Port Staying Time(h)

Length (L)Breadth (B)Depth (d)Max Cargo (m3)
Ship Design Speed (kt)Ship

M/e Power (kW)
Built Year
IMO
Owner
Operator
Flag
Classification
Built Country
Arr. Port Max. DWTDep. Port Max DWT

Port

Arr. Port Max L, B, d (m)Dep. Port Max L, B, d (m)Dep. Port -Arr. Port Name

Route

Dep. Port – Arr. Port Distances

Predicted

Ship Draught Rate (%)

Predicted

Average Voyage Speed (kt)Train Target

Port Staying Time (h)117For all of the steps, the hyperparameters must be set as priors to optimize the modelby minimizing the cost function of learning from the dataset. The hyperparameters
used to generate the three distinct deep learning models are shown in Table 6.4
Table 6.4 Deep learning hyperparameters
Nodes in Hidden Layer

20

Hidden Layers

40

Activating Function

Max Out Function

Drop Out Rate

0.01

L1 Regularization

0.001

L2 Regularization

0.001

6.2 Result of Shipowner ModelIn the shipowner model, we estimated the draft rate, average service speed, and timein port using deep learning analysis. As an example, the results of the three distinct deeplearning models are shown in below Table 6.5. Hence, the results of the deep learning
errors are illustrated in Table. 6.6.
Table 6.5 Example of prediction result by deep learning
Freight Shipping
RoutePort Hedland‐Chiba一One round Trip

Ship

Draft
rate

Average Voyage
speed (loaded)

Average Voyage
speed (unloaded)

Staying Time
(Loaded)

Staying Time
(Unload)A

88%

11.6Knot11.7Knot3.1 days

1.9 daysB

90%

11.3Knot12.4Knot4.1 days

1.9 daysC85%

11.0Knot11.6Knot3.4 days

1.6 days

As shown in Table 6.6, the average draft rate error using the deep learning method is3.4% for draught rate, 0.2% knots for average service speed, and 0.9 days for port staying
time.
Table 6.6 Result of the shipper model
MethodDraught Rate

Average Service Speed

Port Staying Time

Deep learning

3.4%

0.2 knots

0.9 days118

We next used deep learning estimation results to calculate the number of shipment days,
amount of cargo, and shipment cost. Shipment days were calculated by considering theroute distance, navigation speed, and time in port. The cargo transport volume wascalculated based on the method developed by Kigure et al. [147], and the shipment cost
was calculated using the method from Aoyama et al. [148] using the generated data.
Table 6.7 Examples of estimation results by each owner using prediction results
Cargo shipping request

Ship

Availability

Annual Cost

Annual Transport Volume

2014
Port Hedland ‐Chiba
Iron ore

A

No

-

-

B

Yes

$2,115 x104

147 x104 ton

CYes$2,570 x104

130 x104 ton

6.3 Evaluation of Shipowner Model
To confirm the effectiveness of the shipowner model, we compared the standard
deviation of the estimation result of deep learning analysis with that of the response
surface method.
6.3.1 Comparison using response surface method
To confirm the draft rate, we used the response surface method; like a deep learning
analysis method, the following input and output were set:

Input: ship DWT, length, breadth, depth, draft, service speed, horsepower, year
built, the distance between routes, operator, shipyard, maximum draft, arrival
limit, departure limit, new construction shipbuilding price index, and constraintsof loading and unloading ports e.g. Max DWT, max LOA (m), B (m), and D (m)Output: draught rate during navigation (loading and unloading), etc.

As shown in Table 6.8, the average draft rate error using the response surface method is
5.9%, higher than the result using deep learning analysis.
Table 6.8 Comparison of deep learning vs response surface
MethodDraught Rate

Average Service Speed

Port Staying Time

Deep learning

3.4%

0.2 knots

0.9 days

Response Surface

5.9%

-

119

6.3.2 Threshold of the estimation
The draft rate, average service speed, and time in port are different even when thesame ship operates on the same route. In this paper, the standard deviation of such a caseis set as the threshold of the estimation. The threshold is also known in Table 6.9. These
are calculated by using the actual data of bulk carriers that operate between Australia andJapan from 2013 to 2015. As shown in Table 6.9, the estimation result using deep learning
is better than the threshold although that of the response surface method is worse.
Table 6.9 Comparison of deep learning, response surface, and threshold
MethodDraft Rate

Average Service Speed

Port Staying Time

Deep learning

3.4%

0.2 knots

0.9 days

Response surface

5.9%

-

-

Threshold

3.5%

0.9 knots

1.2 days120

Chapter 7
Operator Model
7.1 Development of Operator Model
The operator model collects estimation results from the shipowner model. The
procedure to determine ship allocation is as follows:

Ship Allocation Model
Shipper Model

Operator Model

Cargo shipment
requestRequest for the estimation

Shipwoner Model

Estimated result（cost, amount of cargotransport, etc.)
Decides the ship allocation
for cargo transportation

Shipment
Cargo shipment
contract

Charter
Agreement

Fig. 7.1 Illustration of Operator Model(1) Calculate the total cost and cargo volume
As shown in Table 7.1 (1), shipowners bid for all shipment requests (Ships A–D)
based on each selected route (Routes A–B) from Shippers A and B. The cost per unit
121

transport volume was calculated by considering the total operation cost and totaltransportation volume t. Operation cost-benefit is calculated using the following formula
described in Equation (5) until Equation (10).
𝑔
𝐹𝐶𝑖 (𝑡𝑜𝑛) = 𝐸𝑛𝑔. 𝑃𝑜𝑤𝑒𝑟(𝑘𝑊) ∙ 𝑆𝐹𝑂𝐶( ⁄𝑘𝑊ℎ) ∙ 𝑆𝑎𝑖𝑙𝑖𝑛𝑔. 𝐷𝑎𝑦𝑠(𝑑𝑎𝑦) ∙24(ℎ⁄𝑑𝑎𝑦 )1000,000(𝑡𝑜𝑛⁄𝑔)(5)𝑔
Where Eng Power (kW) is the ship maximum engine power, SFOCi ( ⁄𝑘𝑊ℎ ) is
assumed ship fuel oil consumption, Sailing Daysi (day) is the ship sailing days, and FCi(ton) is the total fuel consumption of each ship.
𝐹𝑢𝑒𝑙. 𝐶𝑜𝑠𝑡. 𝑎𝑡. 𝑆𝑒𝑎𝑖 (𝑈𝑆$) = 𝐹𝐶𝑖 (𝑡𝑜𝑛) ∙ 𝐹𝑢𝑒𝑙 𝑃𝑟𝑖𝑐𝑒 (𝑈𝑆$⁄𝑡𝑜𝑛)(6)

Where FCi (ton) is the total fuel consumption of each ship, Fuel Price (𝑈𝑆$⁄𝑡𝑜𝑛) is
the average fuel price per ton of selected year, and Fuel Cost at Seai (US$) is the total fuelcost at sea of each ship.

𝐹𝑢𝑒𝑙. 𝐶𝑜𝑠𝑡. 𝑎𝑡. 𝑃𝑜𝑟𝑡𝑖 (𝑈𝑆$) =𝐹𝑢𝑒𝑙.𝐶𝑜𝑠𝑡.𝑎𝑡.𝑆𝑒𝑎𝑖 (𝑡𝑜𝑛)
𝑆𝑎𝑖𝑙𝑖𝑛𝑔.𝐷𝑎𝑦𝑠(𝑑𝑎𝑦)∙ 0.1 ∙

𝑃𝑜𝑟𝑡 𝑆𝑡𝑎𝑦𝑖𝑛𝑔 𝑇𝑖𝑚𝑒𝑖 (ℎ)
24(ℎ⁄𝑑𝑎𝑦 )(7)

Where Fuel Cost at Seai (US$) is total fuel cost at sea of each ship, Sailing Daysi(day) is the ship sailing days, Port Staying Timei (h) is the ship port staying time, and Fuel
Cost at Porti (US$) is total fuel cost at the port of each ship. Within this formula, assumed
10% of port staying time, such as waiting to enter the port area or exit the port area, a ship
used its fuel before or after loading/unloading activity.
𝑇𝑜𝑡𝑎𝑙. 𝐹𝑢𝑒𝑙. 𝐶𝑜𝑠𝑡𝑖 (𝑈𝑆$) = 𝐹𝑢𝑒𝑙. 𝐶𝑜𝑠𝑡. 𝑎𝑡. 𝑃𝑜𝑟𝑡𝑖 (𝑈𝑆$) + 𝐹𝑢𝑒𝑙. 𝐶𝑜𝑠𝑡. 𝑎𝑡. 𝑆𝑒𝑎𝑖 (𝑈𝑆$)(8)

Where Fuel Cost at Porti (ton) is total fuel cost at the port, Fuel Cost at Seai (ton) is
total fuel cost at sea, and Total Fuel Costi (US$) is total fuel cost in the single laden voyage.
𝐶𝑎𝑟𝑔𝑜 𝐼𝑛𝑐𝑜𝑚𝑒𝑖 (𝑈𝑆$) = 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡𝑎𝑏𝑙𝑒𝑖 (𝑡𝑖𝑚𝑒𝑠) ∙ 𝑉𝑖 (𝑡𝑜𝑛) ∙ 𝐹𝑎𝑖𝑟𝑖 (𝑈𝑆$⁄𝑡𝑜𝑛)

(9)

122

Where Transportablei (times) is transportable times of a ship to a certain port, Vi (ton) is
the cargo volume estimation from Equation 2, Fairi (𝑈𝑆$⁄𝑡𝑜𝑛) is freight rates per ton
LNG cargo, and Cargo Incomei (US$) is the estimated total income earned.
𝐶𝑜𝑠𝑡­𝑏𝑒𝑛𝑒𝑓𝑖𝑡𝑖 (𝑈𝑆$) = 𝐶𝑎𝑟𝑔𝑜 𝐼𝑛𝑐𝑜𝑚𝑒𝑖 (𝑈𝑆$) − 𝑇𝑜𝑡𝑎𝑙. 𝐹𝑢𝑒𝑙. 𝐶𝑜𝑠𝑡𝑖 (𝑈𝑆$)(10)

Where Cargo Incomei (US$) is total profit earned by a ship, Total Fuel Costi (US$)
is total fuel cost in the single laden voyage, and Cost-benefiti (US$) is estimated profit
earned by a ship.
(2) Calculate the standard deviation
Based on the cost per unit transport volume from the previous step, the standard
deviations of some ships were calculated for each route.

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑜𝑓 𝐶𝑜𝑠𝑡­𝑏𝑒𝑛𝑒𝑓𝑖𝑡 (𝑈𝑆$)𝑖
𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑉𝑎𝑙𝑢𝑒𝑖 = 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝐶𝑜𝑠𝑡­𝑏𝑒𝑛𝑒𝑓𝑖𝑡
(𝑈𝑆$)
𝑖

(11)

Where Average of Cost-Benefiti (US$) is the average cost-benefit of a certain route,Standard Deviation of Cost-Benefiti (US$) is the standard deviation of a certain route, andDeviation Valuei is the deviation value of the selected route.
The average and standard deviation of cost-benefit are including the operation which
has 0 as cost-benefit (transportable times is 0). This is based on the idea that the value ofroutes which possible to be served by fewer ships is higher rather than the route which
possible to be served by more ships. Therefore, the certain route which possible to beserved by 1 ship is more valuable rather than another route which possible to be servedby 10 ships. The deviation value is an index for judging which ship is good fortransporting a given cargo type on a certain route. Table 7.1 (2) shows a sample
calculation of deviation values.
123(3) Ship assignment
Ship assignment decides which kind of ship to charter regularly by considering
standard deviation values. Ships with the highest standard deviation values are assigned
to a shipment on the selected route. For example, as shown in Table 7.1 (2), Ship B is
assigned to Route A2.
Table 7.1 Ship allocation process(1)

Calculate the total cost and cargo volume

ShipperA
B(2)

Ship B

Ship C

Ship D

Volume (t)

($/t)

($/t)

($/t)

($/t)

A1

3.5×10

6

14.8

14.1

16.9

19.9

A2

2.0×106

14.7

13.9

16.4

19.4

B1

4.7×10

6

13.6

13

15.1

18.3

B2

6.0×106

13.1

12.6

14.5

18.2

Cargo

Calculate the standard deviation and ship assignment

ShipperA
B(3)

Ship A

Route

Route

Deviation Value

Cargo
Volume (t)Ship A

Ship B

Ship C

Ship DA1

3.5×106

57.2

60.31

47.9

34.59

A2

2.0×10

6

56.64

60.43

45.58

34.35

B1

4.7×106

56.81

59.74

49.51

33.92

B2

6.0×10

56.84

59.12

50.45

33.58

6

Recalculate the amount of cargo shipment request

ShipperA
B

Cargo

Ship A

Ship B

Ship C

Ship D

Volume (t)

($/t)

($/t)

($/t)

($/t)

A1

3.5×106

14.8

―

16.9

19.9

A2

0.6×106

14.7

―

16.4

19.4

B1

4.7×10

6

13.6

―

15.1

18.3

B2

6.0×106

13.1

―

14.5

18.2

Route

(4) Recalculate the amount of cargo shipment requests
When a shipment is assigned to a selected route, as shown in step 3, the remaining
cargo shipment is calculated by subtracting the amount of cargo shipment requested by
the shipper. Therefore, after assignment, the amount of cargo to ship is updated and Steps(1–3) are repeated until all cargo is successfully transported.
124

7.2 Evaluation of Ship Allocation Model
The confirmation of the ship allocation model proposed in this study is evaluated bychecking the reproducibility of the proposed models i.e. shipper, shipowner, and operator.
The evaluation of the ship allocation model is described in detail as follows:
7.2.1 Problem definition
To evaluate the reproducibility of the proposed model, we simulated ship allocation.
Trade condition (i.e. trade volume, trade routes, and fuel price), allowable ship
specification, number of ships, and port constraints were set as inputs. Then, the resultof the allocation model was compared with actual ship allocation. Moreover, allinformation for simulating ship allocation was extracted from the MLDB. Operation from
Australia to Japan in 2014 was taken as an example.
7.2.2 Simulation results
Fig.7.2 showed the ship allocation for each shipper using the proposed model. As
explained in the previous section, there are four Shippers (A–D). The vertical axis shows
the number of operations (shipments). The horizontal axis shows ship size (in 103 DWT).
Using cluster analysis, the ships are grouped into six clusters: 100, 170, 210, 230, 250,
and 300 (103 DWT). The actual and simulation results are shown together to validate theproposed model.
7.2.3 Discussions
As shown in Fig. 7.2, the simulation results generally agree with actual conditions.
In this section, the allocation process was evaluated:(1) Ports Constraints
In the MLDB, port constraints were generated in two steps:
 Step 1: Extract the constraints from port information. First, port constraints wereobtained from port information. However, some constraints were unavailable or
did not match actual conditions.
 Step 2: Modification using operating data. Port constraints in Step 1 werecompared with actual operations. When the two did not match or when some125

constraints were not available, we modified the port constraints using operatingship specifications.
 Some port constraints are shown in Table 7.2, in which white represents data from
Step 1 and gray represents data modified based on the actual operation (Step 2).

Fig. 7.2 Comparison of actual and simulation results126

(2) Ship Specifications
By examining the actual operation extracted from the MLDB, we identified typicalship specifications for each ship size, which are shown in Table 7.3.
Table 7.2 Port constraints
Shipper

A

B

C

D

Port Name

DWT

L (m)

B (m)

Fukuyama
Chiba
Mizushima
KawasakiKashima
Kisarazu
Oita
NagoyaTobataWakayama
Higashi-Harima
Kure
Himeji

220,000
220,000
260,000
220,000
300,000
300,000
400,000
110,000
160,000
160,000
180,000
276,000
257,000

300
300
340
340
340
330
450
300
327
300
330
360
335

50
50
50
50
60
60
60
43
43
43
47
45
47

d
(m)
18
18
18
18
19
19
25
16
16
14
17
18
16

Table 7.3 Typical ship specifications
DWT

L (m)

B (m)

D (m)

d(m)

HP

106,507

255

43

19

13

16,680177,855

292

45

25

18

22,920210,036

300

50

25

18

21,808229,013

320

54

24

18

30,499250,813

330

57

25

18

29,789297,736

325

55

29

21

30,808127

(3) Allocation process
By Ship, the allocation was started from Shipper C, because its port constraints weremost severe. Hence, B ≤ 43 m and d ≤ 14 m became active constraints, and ships with
100,000 DWT (B ≤ 43 m) were selected. Next, ships for Shipper D were allocated where
B (m) should be less than 45 m. After that, ships for Shipper A were allocated because its
port constraints were more severe than those of Shipper B. Finally, the remaining ships
were allocated to Shipper B.
Based on Fig. 7.2, we see that the simulation results for all shippers generally agreed
with the actual results. Moreover, as shown in Fig. 7.2, Shipper A mostly used 210,000
DWT ships for their operation. Shipper B used various kinds of ships (170,000–300,000
DWT). Since the simulation of ship allocation matched actual ship allocation, and theship specifications agreed with port constraints, we confirmed the effectiveness andreproducibility of the proposed models.

128

Chapter 8
Simulations
8.1 Case Studies
To develop the new ship allocation and examine which specification of the ship is
effective, by considering the present condition based on the data extracted from MLDB
and the future scenario such as fuel price, fuel oil consumption, etc as shown in Fig. 8.1,
we execute the following simulations:

Examination of supply-demand balance

Examination of supply-demand balance with effective ship sizeExamination of supply-demand balance with the influence of fuel efficiency on
demandBased on the data extracted
from MLDB

Future Scenario
Fuel Price
Fuel Consumption

Simulation

Present Condition

1. New ship allocation
2. Effective ship specification

Fig. 8.1 Illustration of the simulation
1298.2 Examination of supply-demand balance
8.2.1 Problem definition
In this study, the supply-demand balance of bulk carriers that operated between
Australia and Japan in 2014 carrying iron ore was examined. In contrast with thesimulation conducted in Section 7.2, we carried out ship simulation without constraints,
meaning that there was no limit to the number of ships per year of operation. The operator
shipped cargo shipments with freely selectable ships. In this restricted example (using theactual number and ship types used in 2014 between Australia and Japan), the simulation
result is defined as supply. In the unrestricted case, the simulation result is defined as
demand and illustrated as shown in Fig.8.2.

INPUT
Freight transport data between iron ore
transport ports, Australia - Japan 2014

Ship Data

Port entry restriction
data
PANAMAX

VOID

Max
12 m

Sea
Level

Cargo is not fully
loaded Ship Restriction

 No Restriction

only existing ship between Australia andJapan is simulated.

SupplyAll bulk carrier in the world
can be used for simulation

Comparison

DemandFig. 8.2 Illustration of the supply-demand balance simulation

1308.2.2 Simulation results
Fig. 8.3 shows the difference in ship allocation results using constraints (supply) andwithout constraints (demand). These results were compared to evaluate ship supplydemand balance and determine the kind of ship likely to be in demand in the future. The
vertical axis is the number of operations (shipments). The horizontal axis is the size ofthe ships (in 103 DWT).
8.2.3 Discussions
As shown in Fig. 8.3, without constraints, the allocation of 210,000, 250,000, and300,000 DWT ships increased. However, the allocation of 170,000 and 230,000 DWT
ships decreased. Therefore, 170,000 and 230,000 DWT ships were not very competitive
for shipments between Australia and Japan. Meanwhile, there were an insufficient supplyof 210,000, 250,000, and 300,000 DWT ships. Hence, these ships are competitive forshipments from Australia to Japan and are expected to be in demand in the future. This
result can be understood from the port constraints shown in Table 7.2.

200

150
100
Operations

SupplyDemand

+54
-31

-45

50

+12

0

+7

50
100
150

200

100

170

210

230

250

300

Ship Size (103 DWT)
Fig. 8.3 Comparison of actual and simulation results
1318.3 Examination supply-demand balance with new ship specification
8.3.1 Problem definition
Based on the discussion in the previous section, 210,000, 250,000, and 300,000 DWT
ships are expected to be in demand. Thus, it is necessary to examine the influence of ship
size on allocation and examine the distribution of ships (210,000, 250,000, and 300,000
DWT) for which increased demand is expected on the selected route (Australia to Japan).
In this simulation, we accounted for the depreciation value of a new ship and ignored thedepreciation value of existing ships. The useful life of a ship was set to 15 years [149],
and depreciation value was calculated based on reference [150].
8.3.2 Simulation results
The principal particulars of 210,000, 250,000, and 300,000 DWT ships are shown in
Table 8.1. Using the proposed method, we examined the principal particulars of the ships.
Moreover, by conducting this simulation we could identify the number of routes and shipsthat could be allocated for the selected route.
Table 8.1 Ship specifications
DWT

L (m)

B (m)

D (m)

d(m)

HP

106,507

255

43

19

13

16,680177,855

292

45

25

18

22,920210,036

300

50

25

18

21,808229,013

320

54

24

18

30,499250,813

330

57

25

18

29,789297,736

325

55

29

21

30,808132

As shown in Fig. 8.4, 300,000 DWT ships were in demand on a single route. In contrast,
250,000 DWT and 210,000 DWT ships can be expected to be in demand on multiple
routes.

250,000 DWT

300,000 DWT

Oita

Oita

2 ships

1 shipKisarazu
1 shipPort Hedland

210,000 DWT

Oita

Kisarazu

2 ships

1 shipPort Hedland

Port Walcott

Port Walcott

Port Walcott

Total Annual
Shipment = 16

Total Annual
Shipment = 16

Total Annual
Shipment = 24

Fig. 8.4 Ship distribution by size
8.3.3 Additional simulations
From the simulation result shown in Fig. 8.4, 250,000 DWT and 210,000 DWT ships
are in demand. To clarify which is preferred, we executed an additional simulation inwhich the fuel efficiency of 250,000 DWT and 210,000 DWT ships increased by 10%.
The results are shown in Fig. 8.5. In Fig. 8.5, the number of allocated ships and routes for
210,000 DWT ships increased rapidly (five additional ships and three additional routes)
as shown in Table 8.2. However, only two additional ships and one additional route werecalled for in the 250,000 DWT ship simulation. Therefore, 210,000 DWT ships have thehighest potential in iron ore transportation between Australia and Japan.

133This result is affected by the port constraints shown in Tables 7.2 and 8.3. Ship of
210,000 DWT can enter all main ports in Australia and Japan and 250,000 DWT ships
cannot enter some key ports.

250,000 DWT

Oita

210,000 DWTKashima
Kisarazu

Oita

Kisarazu
2 ships
1 ship1 ship1 ship1 ship2 ships
3 ships

Port Hedland
Port Walcott

Port Hedland
Port Walcott
Dampier

Total Annual
Shipment = 32

Total Annual
Shipment = 64

1 shipOriginal route
Additional routeFig. 8.5 Ship distribution when fuel efficiency increases by 10%
Table 8.2 Results of ship distribution by efficiency increases 10%

Ship size(DWT ton)

Route

Ship

0%

10%

0%

10%

250000232

4210000

2

5

3

8

134

Table 8.3 Port constraints in Australia
Port Name

DWT

L (m)

B (m)

d (m)

Port Hedland

260,000

330

55

19.2

Dampier

250,000

340

55

19.5

Port Walcott

340,000

335

60

19.5

Parker Point

220,000

300

50

18.5

Esperance

220,000

300

50

18

8.4 Examination of Supply-Demand Balance with the influence of Fuel
Efficiency
8.4.1 Problem definition
To examine the influence of fuel efficiency on ship demand and to draw future
development targets, we simulated increasing fuel efficiency by 5%, 10%, and 15%. A
ship with no fuel efficiency change is defined as S0. Ships with fuel efficiency increases
of 5%, 10%, and 15% are denoted by S1, S2, and S3, respectively. The 210,000 DWT ships
were simulated as they were the most competitive. As in the simulation in Section 8.3,
we considered depreciation values. To evaluate ship effectiveness, we compared thesimulation result (ship replacement) with actual ship allocation.
8.4.2 Simulation results
Table 8.4 shows the simulation result of ship allocation on the intended route(Australia to Japan) after modifying the fuel efficiency of S0, S1, S2, and S3. The tableshows the number of allocated routes, operations, and ships.
Table 8.4 Replacement 210,000 DWT ships
Allocated Routes

Operations

Ships

S0

3

40

5

S1

4

47

6

S2

5

64

8

S3

10

125

16
135

As shown in Table 8.4, the number of operations and ships for S2 and S3 increased
greatly. However, only a small increase occurred for S0 and S1. Therefore, we focused on
ships S2 and S3. The simulation result of increasing fuel efficiency by 10% and 15% isshown in Fig. 8.6.
The vertical axis shows an operation number. The horizontal axis shows the ship size.
In Fig.8.6, the simulation results of ships S2 and S3 are compared with actual ship
allocation.
Shipper A

Operations

150
100
50
0

100

170

210

230

250

Ship Size (103 DWT)

ActualS3S2

S3Shipper B

100

Operations

300

75
50
25
0

100

170

210

230

Ship Size (103 DWT)

250

300
Actual

S2,S3
S2

S3

Fig. 8.6 Comparison of simulation result by increasing fuel efficiency 10% (S2) and
15% (S3) with actual ship allocation
7.4.3 Discussions
As shown in Fig. 8.6, when fuel efficiency increases by 10% (S2), the replacementby 210,000 DWT ships of 170,000, 210,000, 230,000, and 250,000 DWT ships has notoccurred for Shipper A, since the simulation result shows the same number of operations
136

compared with actual ship allocation. Hence, Shipper A will not buy a new ship. However,in contrast, Shipper B will buy a new ship because the operation number increased by 64.
By improving fuel efficiency by 15%, Shippers A and B might each buy a new ship,
as significant replacement occurred for both shippers. For Shipper A, the number of theoperation increased by 35, and for Shipper B, the number of the operation increased by
90. Moreover, as shown in Table 8.4, the total operation number is increased from 64 to
125, and the number of ships from 8 to 16.
In summary, using the proposed model, we simulated ship supply and demand.
Moreover, the principal particulars of the ships expected to be in demand were identified.
In addition, we obtained the impact of fuel efficiency on ship demand.

137

Chapter 9
Conclusions and Future Tasks
9.1 Conclusions
This study presents a ship basic planning support system using maritime logistics big
data. Recently, the era of shipping records big data has been started along with mandatory
digitalized ship movement with AIS (Automatic Identification System). Substantial dataamount is generated by AIS, such as the ship's unique identification of international
maritime organization number (IMO number), position, course, speed, and destination.
Moreover, maritime logistics big data, such as ship and port specification data, route data,international trade data, and data provided by AIS, are currently available and can be used.
Based on that, some studies have been conducted on big data utilization to improve ship
construction, ship operations, and ship maintenance. However, studies regarding theutilization of such maritime logistics big data for ship basic planning or ship design arelimited. Therefore, in this study, we developed marine logistics database to realize theship allocation by conducted a ship allocation model. The MLDB consists of the latest
marine logistics data, i.e., operation information from AIS, ship, port, route, andinternational trading information. The data are managed, integrated, and structured toderive valuable insights from information buried in marine logistics data. Based on the
MLDB, the extracted data are used to construct a ship allocation model. The ship
allocation model constructed aims to replicate the actual ship allocation.

138

The actual shipping market has the essential affection in the actual ship allocated.
The ship allocation model consists of three specified models, shipper model, shipownermodel, and operator model. The shipper model creates the clusters of exporter andimporter ports, and its cargo demand is to be transported in the selected year. Later, thedemand estimated will be used in the operator model as a freight transport request to theshipowner model. Following the request, the shipowner bid the cost and transportable
volume on the route where the shipowner ship operates. To finalize the charter contract
between operator and shipowner, the operator will choose the served bid in economical
consideration and create ship allocation. The next important stage of this study has
conducted the simulation. The simulation was examined supply-demand balance,
examined effective ship size, and the influence of fuel efficiency on demand.
The conclusions of this dissertation are summarized as follows:
1. Marine Logistic Database (MLDB) manages the available data from maritime
industry big data.
2. The error cleaning is performed to maintain the quality of data to developed MLDB
and validation is applied to all the data.
3. The ship allocation model composed of distinct shipper, shipowner, and operator
models was developed. This model was effective in estimating ship supply anddemand, the influence of ship size and fuel efficiency on ship allocation, and theprincipal particulars of ships for which demand is expected to increase.
4. The results of the shipper model show that the cluster results compared between 2014
and 2017 remained the same. Moreover, based on the exporter’s point of view andimporter’s point of view, there are no shipper cluster has changed. The only change
is the amount of cargo transport volume.
5. By using the proposed model, we confirmed the reproducibility of the ship allocation
model. The supply-demand balance, effective ship specifications, and influence of139

ship efficiency on demand could be realized using the ship allocation model proposedin this study.
6. The ship with the most competitive demand on the selected route (Australia to Japan)
for iron ore was the 210,000 DWT ship. In the future, we plan to automate the ship
allocation model to simulate worldwide ship allocation for various cargos, ship sizes,
and ship types.

9.2 Future Works
In this dissertation, the constructed MLDB, ship allocation model, and simulationsdeliver acceptable results, several improvements are possible to be acquired in the future.
Followings are the prospective future works that can be adopted to complement this study:
1.

The target ship of this study is the bulk carriers which are limited for iron ore from
Australia to Japan, and we confirmed the reproducibility of the ship allocation of it.
It is a big challenge to adopt this method for all the bulk carriers that operated fromall over the world with the other cargo type to evaluate the characteristics of the ship
operation, the real ship allocation, and predict the future ship specification in demand
to be expected.

2.

To be more effective, the automation system for the support system of ship basicplanning support using MLDB should be executed.

3.

For the allocation process accuracy, the more detailed port data will give moreadvantages. Because in this study the port data information is limited in the port-levelspecification. With detailed port data information, it will strongly increase the port
limitation accuracy of the ship allocation process.

4.

The future scenario inputted to the developed ship allocation model has no specificperiod. Therefore, it will be advantageous for the ship allocation to be dynamicallyapplicable for the specifically targeted variable in the long-span period, for example
in the next 10 years, 20 years, or further. For that consideration, the mathematicalcorrelation of related variables and suitable approach is necessary to be confirmed.
140

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Acknowledgment
I felt I have learned a lot from writing this dissertation searching for the truth ofscience and life. This is the great treasure I will cherish not only in my future academic
career but in my whole life.
I would like to take the opportunity to express my immense gratitude to all those
persons who have given their invaluable support and assistance.
First, I would like to express my utmost gratitude to my research supervisor Prof.
Kunihiro Hamada, for his sincere and continuous support, prompt and useful advice, andpatience during my doctoral study. His immense knowledge and plentiful experience haveencouraged me in all the time of my academic research and daily life.
Second, I would like to express my sincere gratitude and my thank fullness to my
sub-adviser, Assistant Professor Noritaka Hirata, and Prof. Mitsuru Kitamura.
My stay and studies in Japan have been supported by the scholarship of the Japanese
Ministry of Education, Science, Sport and Culture MEXT-ASCOJA, for which I am verymuch grateful.
I very much acknowledge the help of the staff of the offices of the Graduate Schoolof Engineering. As well, I am thankful to all my fellow students in the Laboratory ofTransportation System Innovation.
Finally, I would like to express my gratitude to my parents, my big family for the
support and continuing prays so far. I am very much thankful to my wife Yorida Agustin
Kurniayu and my son Keindra Arsakha Shakeel Althafunizam for their love. Without
their tremendous understanding and encouragement in the past few years, it would be
impossible for me to complete my study. Love you all.

159