2209

SIMULATION ANALYSIS OF FREIGHT DELIVERY SYSTEM WITHIN
A COMMERCIAL OFFICE BUILDING
Nashreen G. SINARIMBO
Graduate Student
Dept. of Info. Engineering and Logistics
Tokyo University of Mercantile Marine
2-1-6 Etchujima, Koto-ku,
Tokyo 135-8533, Japan
Tel/Fax: +81-03-5620-6492
E-mail: nash@ipc.tosho-u.ac.jp

Yoji TAKAHASHI
Professor
Dept. of Info. Engineering and Logistics
Tokyo University of Mercantile Marine
2-1-6 Etchujima, Koto-ku,
Tokyo 135-8533, Japan
Tel/Fax: +81-03-5620-6492
E-mail: takaha@ipc.tosho-u.ac.jp

Tetsuro HYODO
Associate Professor
Dept. of Info. Engineering and Logistics
Tokyo University of Mercantile Marine
2-1-6 Etchujima, Koto-ku,
Tokyo 135-8533, Japan
Tel/Fax: +81-03-5620-6492
E-mail: hyodo@ipc.tosho-u.ac.jp
Abstract: This paper tries to assess the effect of cooperative delivery system to one of thebuildings inside Marunouchi District in transporting goods to different floors. Several models
are developed to provide alternatives to the present delivery system. The data used on the
study was taken from actual experiment of TDM implementation enforced in Marunouchi in
Feb 2002. The said experiment aims to improve the traffic flow and to reduce the alarming
increase of harmful chemicals emitted by vehicles particularly from trucks. The paper also
tries to examine the potential and accuracy of PEAMON in monitoring activities of workers
employed to deliver the goods. Results tend to suggest that the two workers employed in thebuilding are under utilized while PEAMON shows promising results in monitoring workers
activities that leads to the identification of the location of goods.
Key Words: ProModel, PEAMON, Marunouchi, simulation, cooperative delivery
1. INTRODUCTION
Marunouchi District, located beside Tokyo Station and one of the biggest business districts inTokyo, is undergoing a major renovation. Its old commercial office buildings are remodeled
and converted into high-rise to accommodate economic and technological demands. Also
included in this infrastructure renewal is the improvement of its avenues. The redevelopment
project is designed to cater different types of businesses such as media companies, financial
institutions, foreign firms, and shopping district.
An actual experiment of TDM implementation was enforced for about a month in early 2002.
One component of the experiment is a 30-minute free parking charge located at theunderground floors of most of the buildings. This strategy is designed to reduce the number ofvehicles usually parked illegally on the already congested streets. Another component of theexperiment was the enforcement of cooperative delivery of goods coming into the area. A
stock-point station was set up around 500-meters away. Aside from decreasing the number ofillegally parked vehicles, another concern of the experiment is to reduce the continued risingof nitrogen oxide (NOx), suspended particulate matter (SPM) and other harmful chemicals
emitted from motor vehicles, particularly from trucks.

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While the experiment was in place, coordinated surveys were conducted. These surveys weremeant to document the effect of the experiment to the behaviors of vehicles and to the methodof the delivery of goods. Data obtained from the surveys will then be use to assess the overall
impact of the experiment. If the experiment found to be effective, it will be enforced orotherwise revised. The data used on this study was taken from these surveys particularly fromthe survey of cooperative delivery system. Although complete set of data is available, thisstudy will only focus on the effect of cooperative delivery practice on vertical movement ofgoods inside the building. In particular, this study will assess the performance rate of theworkers assigned to facilitate the movement of goods inside the building and evaluate thepotential of the surveillance device used in gathering the data.
Urban freight transport and logistics studies recently gained wide recognition mainly due totheir economic role and concern to their impact to the environment. However, still, it is notthat usual to locate a study wherein the relationship of road traffic (delivery truck) and theoffloading chain inside the building is clarified. This study, aside from widening ourunderstanding to goods movement as general, hopes to contribute in the effort to come up amethod that could accurately document the activities involve in transporting the goods. This
newly introduced method of documentation is significant to clarify the functions of logistics
elements – workers, elevator, docking area- that could determine the efficiency of the system.
2. LITERATURE
The significance of understanding the whole process of delivery of goods inside CBD has
become more important since many studies point out that delivery trucks have a high share ofparticipation for the traffic problems. Incidents such as concern for safety, sharp rising of NOx,
CO2 and other negative effect to the environment, noisy and producing strong vibration are allfamiliar attributes to goods vehicle. In trying to address these problems, several study
approaches has been made accounting series of actions required to transport goods.
Simulation analysis approach is one familiar strategy and perhaps the most effective tool in
explaining the material flow. Park et al (1998) simulated the three types of loading/unloading
station, i.e. street parking, off street parking, and docking area inside the building, andcompare the time spent by each vehicle before leaving out. The offloading chain was also
simulated using WITNESS simulation package. The research concluded that there were only
very slight difference between the results of simulation and done manually thus the authorsaffirmed that simulation approach could explain the whole supply chain.
In New York, Morris et al (2001) found out that a freight vehicle in the central business
districts averaged 33 minutes to unload goods both at the side of street and at the dock locatedat the underground floor of the building. This study concluded that often time, the limited
capacity of freight elevator causes the longer time delivery or in worst case, the building lacks
this freight facility. Situations like these force the goods carriers to utilize the stair or mix up
with the passengers in the passenger elevator further prolonging the delivery time.
Ma, Liying (2001) organized the variables necessary for simulation model of loading andunloading of goods in her studies entitled “Urban Goods (Off) loading Chain”. The paper israther a conceptual, identifying the activities and formulates the structure of the simulation
model. The study appears to be complementary to the study done by Park et al noting thatmost of the variables except “hindrance”, “total emission”, “noise level” were already

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

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included to the simulation study of Park. Nonetheless, this study presented a good conceptual
analysis on how to tackle the goods processes for simulation inside a commercial building.
Recent years paved way to the evolution of several activity monitoring devices. One of theseis the PEAMON (Personal Activity Monitor) which although newly developed surveillance
device but already gained wide recognition. Asakusa et al produces several papers exploring
the potential and accuracy of this device in monitoring the activity of individual holders. For
instance, “Monitoring Personal Travel Behavior using a Cellular Phone System with Power
Antennas and CSID Analysis” traces the routes of PEAMON holders and explain each mode
involve in his route. Tanaka et al (2001) also used PEAMON in his study entitled“Reconstruction of Spatio-temporal Distribution of Event Visitors by Fusing Multi-source
Data”.
3. SURVEY IN DETAIL
3.1 Surveyed data

Table 1. No. of truck trips and goods delivered (2/19/2002)

The data obtained fromtrucks delivering goods
during the three (3) day
experiment are shown in
Table 1, 2
and
3.
Observations suggest thattime interval from each trip
in the early morning delivery
is very short - for instance isthe time interval between thefirst trip and second trip on
Table 1. The first delivery
truck arrived at 8:48
carrying 14 goods andfollowed shortly by anothervehicle transporting 16
goods at 8:52 resulting to 4
minutes
timeinterval
between the trips. It isbelieved that in this part ofthe day, most businessestablishments and offices
are storing goods supplies
that could at least serve thewhole day. This might be
the primary reason that
could explain on why there
is a big amount of goods toeach trip having very shorttime interval.

Date

Arrival time

No of goods

2/19

8:48

2/19

8:52

16

2/19

8:53

32

2/19

9:04

39

2/19

9:22

4

2/19

9:39

1

2/19

14:56

2

14

Table 2. No. of truck trips and goods delivered (2/20/2002)
Date

Arrival time

2/20

8:37

No of goods
13

2/20

8:46

30

2/20

8:47

2

2/20

9:07

6

2/20

9:10

36

2/20

9:24

12

2/20

10:08

1

2/20

10:48

2

2/20

11:11

2

2/20

13:09

1

2/20

14:04

1

Table 3. No. of truck trips and goods delivered (2/21/2002)
Date

Arrival time

2/21

8:36

No of goods
11

2/21

8:46

31

2/21

8:46

2

2/21

9:09

9

2/21

9:17

35

2/21

9:47

8

2/21

13:09

12

As far as vehicle trip isconcern, there are seven (7) trips on Feb 19, eleven (11) trips on Feb 21 and seven (7) trips on
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Feb 21. With regard to the number of goods delivered, total number of goods reached thebuilding on Feb 21 is 108, 106 on Feb 20 and108 on Feb 21. The number of goods arrived thebuilding on Feb 19 and 21 have the same amount, which interestingly produced the same
number of truck trips.
3.2 PEAMON as a tool in tracing the delivery floor of goods
3.2.1 Description of PEAMON

Asakura et al (2001) describe it as a pocket-size travel datacollection equipped with PHS (Personal Handy phone
System) receiver, three-dimensional acceleration sensor,
CPU, compact flash memory card and powered by lithium
ion battery (Figure 1). The unit’s size is 120 mm in height,
12 mm in thickness, and has a weight of 125 gram. The
location positioning data (longitude, latitude, and time) is
generated every 15 seconds via its built-in PHS function.
This travel-monitoring unit uses the signal strength and the
ID number of 3 to 7 base stations of PHS communication
system capable of monitoring activities for 48. Its
durability is further enhanced by a special function called“turns to sleep mode” when the acceleration signal is weak.

PHS function
Acceleration
sensorFigure 1. PEAMON modelshowing its built-in equipments

3.2.2 Monitoring the Goods Offloading Chain
PEAMON’s signal is relying to the power of base station and therefore
cannot record with the absent of signal. In order to mitigate this difficulty,a Power Antenna (PA) with range of 300 meters was installed to eachfloor of the building totaling to 19. These PAs, which solely developed tosupport PEAMON function, were installed sequentially in order to have asequential base station ID. PEAMON will record the ID of each stationand its corresponding signal strength to distinguish it from one floor toanother.

PA 5
Floor 5PA 4
Floor 4
PA 3
Floor 3
PA 2
Floor 2The installation of PAs is illustrated in Figure 2 while example of basestation ID is shown in Table 4. As can be seen from the table, each basestation has a unique identification number making it simple to trace
which floor the unit holder located. Interpreting the table would state thatthe unit holder was in Floor 1 from 9:10:00 until 9:10:15 and moves to
Floor 5 at 9:10:30 and arrived to Floor 4 at 9:11.

PA 1
Floor 1Figure 2. Sequential
installations of PAs

The monitoring of the activities of the two workers inside the building assigned to transport
the goods work in this way: they will write to the logbook their destination floors, number ofoffices intended for delivery and the number of goods going to deliver every time they leave
the docking area located at the basement (B3) floor of the building. The PEAMON cannot
record data while the holder is inside the elevator due to the absence of signal. However,PEAMON monitoring will resume as soon as the elevator opened up to the destination floor.
Data will be recorded continuously every 15-seconds until they return again to the elevator.

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Table 4. Example of station ID and signal strengthTime
9:10:00
9:10:15
9:10:30
9:10:45
9:11:00

PA1 (ID, SS)
1, 12
1, 20

PA2 (ID, SS)

PA3 (ID, SS)

PA4 (ID, SS)

PA5 (ID, SS)
5, 40
5, 25

4, 16

The result of PEAMON
monitoring is presented in
two ways. The first one is in
the form of graph showing
the routes of both workers
starting from their firstdelivery in the morning
until their last delivery at
the end of the day. The
second one is in the form oftables which clarify theindividual route of theworkers shown in the graph.
Every single graph iscomposedoftwo
supplemental tables.

Floor Number

3.2.3 Results of PEAMON monitoring
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
-1
-2
-3
-4
8:00

Worker1

9:00

10:00

11:00

12:00

13:00

14:00

Worker2

15:00

16:00

Time

Figure 3. 2/19 PEAMON monitoring result

Table 5. Worker1’s route
Time (Feb 19)
9:00

9:25

9:50

10:15

10:40

Route
B3 F1 B3

Floor Location

No of office

No of delivery items

DestinationTime

1Floor

1

10

B3 F1
F1
F1 B3

0:00:09
0:01:51
0:00:09

B1

1

19

B3 B1
B1
B1 F1

0:00:08
0:01:25
0:00:05

1Floor

2

5

F1
F1 B3

0:00:210:00:09

7Floor2

94Floor1

2

3Floor

1

8

B3
F7
F7
F4
F4
F3
F3

B3 B1 F1 B3

B3 F7 F4 F3 B312Floor1

1

10Floor1

2

2Floor1

3

B3 F12 F10 B3

B3 F2 B3

F7

B3

0:02:43
0:03:51
0:00:09
0:01:55
0:00:05
0:00:25
0:07:50

B3 F12F12F12 F10F10F10 B3

0:00:25
0:02:52
0:00:08
0:02:30
0:13:15

B3 F2
F2
F2 B3

0:00:11
0:02:19
0:00:11

F4
F3

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The monitoring result of PEAMON on the first day (Feb 19) of experiment is shown in Figure
3. The delivery activity started by Worker1 at 9 in the morning and followed shortly byWorker2 at 9:15. The first delivery stop of Worker1 was on Floor 1 and his last delivery was
as early as 10:40 - brings three (3) pieces of goods to Floor 2. On the other hand, Worker2’s
first stop was on Floor 5 and ends his delivery after sending goods to Floors 4 and 1 at 2:55.
The detail of the routes of Worker1 and Worker 2 is shown in Table 5 and 7 respectively.
Table 6. Worker2’s route
Time (Feb 19)

Route

Floor Location

No of office

No of delivery items

DestinationTime
0:01:13
0:03:45
0:04:15

9:15

B3 F5 B3

5Floor

1

12

B3 F5
F5
F5 B3

9:35

B3 F2 B3

2Floor

1

7

B3 F2
F2
F2 B3

0:00:11
0:04:49
0:00:1115Floor1

1

B3 F15F15

0:01:57
0:00:23

10Floor

1

3

7Floor

1

4

B1

1

2

F15 F10F10F10 F7
F7
F7 B1
B1
B1 B3

0:00:37
0:06:210:00:09
0:04:53
0:04:37
0:03:38
0:00:07

2Floor

2

6

B2

1

1

B3 F2
F2
F2 B2
B2
b2 B3

0:00:11
0:01:53
0:03:07
0:00:55
0:00:05

B1

1

11

B3 B1
B1
B1 B3

0:00:07
0:01:53
0:00:07

4Floor1

1

1Floor

1

1

B3 F4
F4
F4 F1
F1
F1 B3

0:00:49
0:00:51
0:00:09
0:00:51
0:00:09

10:20

10:40

14:55

B3 F15 F10 F7 B1 B3

B3 F2 B2 B3

B3 B1 B3

B3 F4 F1 B3The
seconddayof
PEAMON monitoring result
is depicted in Figure 4. The
route network of Worker1
started at 9:35 delivering
goods to Basements 1, 2,
and ends as early as around11:54 after completing todeliver the goods for
Basement 4. For the case ofWorker2, his route network
startedat
9:45
andterminated at around 2:07 in
the afternoon after having
delivered the last goods to
Floor 7. Although there
were several

Floor Number

9:50

15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
-1
-2
-3
-4
8:00

Worker1

9:00

10:00

11:00

12:00

13:00

14:00

Worker2

15:00

16:00

Time

Figure 4. 2/20 PEAMON monitoring result

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Table 7. Worker1’s route
Time (Feb 20)

9:35

Route

Floor Location

No of office

No of delivery items

B1

1

24

B2

1

1

1Floor2

18

2Floor1

2

1Floor1

5

B3 B1 B2 B3

9:55

B3 F1 B3

10:20

B3 F2 F1 B3

10:50

B3 B1 B3

B1

1

7

11:10

B3 B4 B3

B4

1

2

No of office

No of delivery items

10Floor1

1

8Floor1

4

6Floor1

1

4Floor2

5

Destination
B3 B1
B1
B1 B2
B2
B2 B3
B3 F1
F1
F1 B3
B3 F2
F2
F2 F1
F1
F1 B3
B3 B1
B1
B1 B3
B3 B4
B4
B4 B3

Time
0:00:07
0:00:56
0:00:05
0:00:55
0:00:05
0:00:09
0:01:210:00:09
0:00:11
0:10:37
0:01:53
0:02:06
0:00:09
0:00:08
0:02:55
0:20:20
0:00:05
0:44:25
0:00:05

Destination
B3 F10F10F10 F8
F8
F8 F6
F6
F6 F4
F4
F4 B3
B3 F7
F7
F7 B3
B3 F4
F4
F4 B3
B3 B1
B1
B1 B3
B3 F7
F7
F7 B3

Time
0:00:49
0:06:23
0:00:07
0:02:55
0:03:35
0:00:23
0:00:07
0:02:47
0:00:13
0:00:19
0:02:25
0:07:35
0:00:41
0:01:22
0:00:38
0:00:08
0:01:52
0:00:08
0:01:13
0:01:12
0:00:18

Table 8. Worker2’s route
Time (Feb 20)

9:45

Route

Floor Location

B3 F10 F8 F6 F4 B3

10:15

B3 F7 B3

7Floor

4

14

10:30

B3 F4 B3

4Floor

1

1

13:10

B3 B1 B3

B1

1

1

14:05

B3 F7 B3

7Floor1

1

rising of both networks of the two (2) workers as shown in the graph, it does not suggest that
all the trips were made to deliver goods. The workers made several trips to other floors which
when verified to the logbook do not show any delivery of goods involved.
The two tables complementing the routes shown in Figure 4 are Table 7 and 8. Table 7
explains the route network of Worker1 on the second day of the survey while Table 8 details
the route of Worker2. As can be observed from Table 7 there are three (3) recorded time
appear to be long enough to warrant a further clarification. First is the 10 minute and 37
second time spent in delivering goods at the 10:20 trip. A look at to PEAMON data, however,shows that this worker spend this whole time in Floor 2 without moving to other floors. This
gives the impression that the worker was made to wait for some time before the receiver wasavailable. The second case needs to be clarify because of the unusual longer time spend indelivering goods is the 10:50 trip. Data recorded by PEAMON shows that the worker after
Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

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having delivered the goods for Basement 1, went to Floor 1 without carrying goods before
finally returning to docking area. The third case, which raises concern due to spending moretime than thought to be required, is the 11:10 trip. Shown in the PEAMON data that thisworker spend the whole 44 minutes and 25 second in Basement 4. There must be unusualinterruption which cause him to spend more time in facilitating this particular delivery.
Worker2’s first trip chain as reflected in Table 8 revealed that he delivered goods
continuously to Floor 10, Floor 8, Floor 6, and Floor 4. The longest delivery time wasobserved in the 10th floor, where one (1) piece of goods delivering took 6 min and 32 sec.

Floor Number

On the third day ofexperiment,
PEAMON
15
Worker1
Worker2
14
recorded data is illustrated
13
in Figure 5. Worker1 made12
11
his first delivery to Floor 5
109
at 9:15 while Worker2 had
8
7
delivered nine (9) pieces of6
5
goods to Floor 1 ten (10)43minutesearlier.
Both2
1
workers have their last0
-1
departure from the docking-2-3
area at 1:10 sharing the-4
freight elevator. Although
8:00
9:00
10:00 11:00 12:00 13:00 14:00 15:00 16:00
the same figure suggested
Timethat Worker2 still made one
more departure at around 3
Figure 5. 2/21 PEAMON monitoring resulto’clock in the afternoon,records in the logbook
shows that this trip was not related in transporting goods.
Table 9. Worker1’s route
Time (Feb 21)
9:15

10:00

Route
B3 F5 B3

Floor Location

No of office

No of delivery items

5Floor1

10

2Floor1

4

1Floor1

2

2Floor1

4

1Floor

1

6

B3 F2 F1 F2 F1 B3

10:40

B3 B1 B3

B1

1

2

13:10

B3 F3 B3

3Floor1

1

Destination
B3 F5
F5
F5 B3
B3 F2
F2
F2 F1
F1
F1 F2
F2
F2 F1
F1
F1 B3
F3 B1
B1
B1 B3
B3 F3
F3
F3 B3

Time
0:00:30
0:03:40
0:00:50
0:00:11
0:00:25
0:00:05
0:02:10
0:00:05
0:03:55
0:00:05
0:05:06
0:00:09
0:00:08
0:01:22
0:00:08
0:00:12
0:00:48
0:00:12

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Table 10. Worker2’s route
Time (Feb 21)
9:10

9:45

10:15

13:10

Route

No of office

No of delivery items

1Floor1

97Floor4

17

6Floor1

2

4Floor2

6

15Floor1
1

1
1

10Floor1

3

B3 F15 F10 F8 F7 F6 B
8Floor3

1

1

7Floor1

1

6Floor

1

1

B1

1

11

B3 F1 B3B3 F7 F6 F4 B3

B3 B1 B3

Floor Location

Destination
B3 F1
F1
F1 B3
B3 F
F7
F7 F6
F6
F6 F4
F4
F4 B3
B3 F15F15F15 F10F10F10 F8
F8
F8 F7
F7
F7 F6
F6
F6 B3
B3 B1
B1
B1 B3

Time
0:00:09
0:00:51
0:00:09
0:00:18
0:00:25
0:00:05
0:00:25
0:11:05
0:00:25
0:08:05
0:00:55
0:00:52
0:00:38
0:09:22
0:00:08
0:01:25
0:00:05
0:01:55
0:05:35
0:00:43
0:00:17
0:00:08
0:02:25
0:00:35

The details of each trip chain by the two workers graphed in Figure 5 are shown in Table 9
and 10. Worker1 actually visited and made delivery to seven (7) floors of which he spent thelongest stay at Floor 6. On the other hand, Worker2 made delivery to eleven (11) floors
visited 15 offices scattered to the floors building.
3.3 Summary result of the survey
The survey reveals several insightsthat can be useful in understanding
the complexity of goods offloading
chain. The arrived data averages andproperties of elevator are shown in
Table 11. Opening and closing timeof elevator is 3.5 sec and 4.9 sec
respectively.
Furthermore,theelevator time for movement fromone floor to next immediate floor(either up or down) is 5 sec.
However, when the desired location
is higher than that – say from firstfloor going to 5th floor – the elevator
time from first floor to second floor
is 5 second, while from second floor
to 3rd floor is 2 second (Table 11).

Table 11. Averages observed from the survey
Floor

Elevator
time

Ave
time

Ave no of
officeAve no of
goods

No of
trip

15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
-1
-2

30.0
28.5
27.0
25.5
24.0
22.5
21.0
19.5
18.0
16.5
15.0
13.5
12.0
10.5
9.0
7.0
5.0

0:00:37

1.0

1.0

2

0:02:52

1.0

1.0

1

0:06:09

1.0

2.3

4

0:02:10
0:02:27
0:00:30
0:03:43
0:01:28
0:00:36
0:04:00
0:01:50
0:02:03
0:00:55

1.0
2.2
1.0
1.0
1.4
1.0
1.2
1.3
1.0
1.0

2.5
7.7
1.3
11.0
3.0
5.0
4.3
7.0
9.6
1.0

2
6
3
2
5
2
6
8
8
2

-3

0.0

DOCKING AREA

-4
5.0
0:44:25
1.0
2.0
1The processing time for 13 goods(average per trip) at the docking are(B3) is 8 minutes. This time is refers to the moment the two workers work on the goods from

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

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the vehicle, sort out, divide the amount andidentify each other’s route up to their departure by
the elevator. For instance, if the delivery is to bemade at Floor 15, the computed time will looks
like this- 8 minutes (processing time) plus 30 sec(elevator time) plus 8.4 sec (opening and closing
time of elevator) plus 37 sec ( average delivery
time on that floor). This case assumes that theelevator is uninterrupted to the other floor and thefirst stop is the 15 floor.
The lack of available data concerning the sizes
and weights of goods and the distance of offices
from elevator make it difficult to explain the realcause on why some delivery time takes longer
than the others do. Although logic might state thatthe amount of goods delivered and number ofoffices visited is directly proportional to theamount of time spent for delivery, it is also
important to consider the distance of offices fromthe elevator. For instance, it would be difficult to
explain on why the average delivery time to Floor
5 is shorter than say Floor 10 given that Floor 5
has more number of goods to be delivered. One
possible explanation is perhaps the office visitedin Floor 5 is closer to elevator than that of Floor
10.

EXISTING / PLANNED FACILITY•••BUILDING THE MODEL
Define locations, raw materials
Develop routing, processing rule
Identify parts and materials arrival
RUN SIMULATIONREVIEW OUTPUT

WHAT IF . .

•We add locations and resources•We reduce batch size
•We change arrival of raw material•We change the selection routines
IMPLEMENT CHANGESRESULTS•Reduced WIP (work-in-process)•Increase throughput•Reduced operating expenses
Figure 6. Process of building
simulation model

The data of Basement 4 is consider irregular and was taken out during the computation for
average delivery time to each floor which used as input data for simulation. There was astrong indication that there were interferences during the delivery to B4 and the time recorded
did not reflect the actual delivery time.
4. MODELING
Several models are presented in this section depicting different scenarios. The actual counted
data is applied to all the models and the effectiveness of having only one worker transporting
goods has been modeled. In addition, the different interarrival times of truck trips such as 15
minutes and 30 minutes were also modeled separately to observe their effect. This isimportant to gained better understanding on the different variables that can influence thesystem’s efficiency.
4.1 Process of building simulation model
Shown in Figure 6 is the process of building a simulation model in ProModel. This simulation
software used five important elements to describe either existing or planned facility. Locations
represent places where raw materials arrived or process. Raw material is anything that moves– for this study, the entity is goods. After having the two elements, the next step is to develop
the routes used by entity at the same time connects one location to another. Processing rule is

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

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set to define what the modelers want the entity to do while at a location. Processing time andother ready commands can be issued in processing rule. The last element is the setting ofarrival time and interarrival of raw materials. Several arrival distributions are available thatcan be applied. If the arrival or interarrival is complicated and cannot be easily set say 5 or 10
minutes, then StatFit statistical software included in ProModel can be used to identify which
distribution best fit the data.
After completing these processes, the modelers may run the simulation and review the output.
Modification can easily be made say adding or subtracting number of locations or resources.
When the modelers finished the modifications, the simulation can be run repeatedly until
desired result is attained.
4.2 Data Fitting
Table 12. Distributions Fit
Since
the Distribution
Rank
AcceptanceK-S test
A-D test
surveyed data Inverse Gaussian (-3.35, 23.6, 40.1) 100
acceptDo not reject Do not reject
99.9
acceptDo not reject Do not reject
do not follow Lognormal (-0.483, 2.93, 1.3)
Pearson 5 (-7.7, 1.75, 39.4)
99.4
acceptDo not reject Do not reject
any pattern, for Weibull (1, 0.682, 28.8)
98
acceptDo not reject Do not reject
96.2
acceptDo not reject Do not reject
instance
15 Exponential (1, 35.7)
Erlang (1, 1, 35.7)
96.2
acceptDo not reject Do not reject
minutes
Do not reject Do not reject
Gamma (1, 1, 35.7)
96.2
accept91.9
acceptDo not reject Do not reject
interarrival, it is Pearson 6 (1, 102, 1.27, 4.23)
Log-Logistic (1, 1.51, 23.4)
87
acceptDo not reject Do not reject
not possible to Beta (1, 4.95e+6, 0.984, 1.23e+05)
Do not reject Do not reject73.4
accept7.37
acceptNo fitNo fitinput
afix Pareto (1, 0.35)
Triangular (0.951, 177, 0.986)
0.898
acceptDo not reject reject
interarrival. To Uniform (1, 158)
0.0027 reject
rejectrejectmitigate
thisdifficulty, the perceived appropriate way to determine the best distribution that could
represent the interarrival is the use of StatFit. This statistical software is available to ProModel
simulation package. The collected data for arrival times of trucks in three-days were worked
in Excel spreadsheet and plugged into the statistical software. After running the software,
however, only the second day-data generated distributions fit and the two other arrival data
were rejected due to insufficient of data point.
The suggested distributions to represent the interarrival times are shown in Table 12. Several
distributions fitted the data which could be applied to interarrival of trucks trips. Normally,
chi-square test is applied to see if the data came from a specific distribution. However, lack ofadequate number of data points prevented from performing this test. Two other useful tests are
Kolmogorov-Smirnov test (K-S test) and Anderson-Darling test (A-D test). Like the chisquare test, these tests are used to decide if the sample comes from a specific distribution. The
A-D test is a modification of K-S test and gives more weight to the tails than does the K-S test.
Result of K-S test, at .05 significance level, shows that only the Uniform distribution was
rejected. On the other hand, the A-D test aside from rejecting the Uniform distribution also
rejected the Triangular distribution. Both tests did not give value to the Pareto distribution.
The lack of adequate data is seen as the main reason for the vague results of the tests. (Detailsof K-S test and A-D test were not shown for brevity).
Statistically, any of the remaining ten distributions could be applied for time intervals between
trips. However, it is important to determine which distribution could provide an output thatwould reflect the real behavior of the system. Aside from applying all the accepted
distribution to the simulation (figure 8 shows the result of exponential distribution), another

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method is by plotting the input data against the selected distribution as reflected in Figure 7.
As shown in the figure, the shape of the data is the same as the plot of exponential distribution.
More than the methods employed to identify the fitted distribution to the data, literature often
mention that exponential distribution often provides a good approximation of interarrival
times in waiting line situations. This distribution is frequently used to represent the time
between random occurrences, such as the time between arrivals at a specific location in aqueuing model or the time between failures in a specific operation.
4.3 Description of the simulation model
The simulation models built were done using
ProModel Version 4.1, a commercially available
discrete event simulation package used for
simulating manufacturing system. The entire
process involved in transporting the goods istherefore modeled as a manufacturing network
where each floor of the building, docking area,
stock point are considered as locations. The goods
coming in to the building is assigned as entity and
Figure 7. Fitted arrival distribution
the two workers and the trucks considered asresources. Elevator was not represented throughresources due to perceived difficulty of handling it in the software since a resource (elevator
for instance) cannot transport another resource (say worker 1). However, the network on
which the worker/s travels was model the same function as the elevator.
For modeling convenience, certain assumptions were set:••

No distinction is made between
types, sizes, and weights of cargo.
From the floor where the delivery
is to be made, it took 2 minutes
and 24 seconds for worker/s toreturn to the elevator disregarding
the number of offices and goods(this is obtained by getting the
average of all floors’ averages
monitored by PEAMON).
The required time for two workers
to unload 13 goods, sort, and othernecessary movement up toentering elevator is 8 minutes(average obtains from PEAMON
monitoring).

35

Actual data30
No of Goods•

Model 125

Model 2

20
15
10
5
0
1

3

5

7

9

11

13

15

17

19

Building Floor

Figure 8. Actual data vs Models result

4.4 Properties of the models
All the models differ only to the variables number of workers and interarrival distribution. As
shown in Table 13, in terms of the variable number of worker, only Model 1 is characterized
by one worker and the rest models have two workers. This model is developed to test how it
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2221

would fare if only
Table 13. Properties of built models
oneworkeris
Variables
Model 1Model 2Model 3Model 4
employed with the
No of worker
12
2
2
same amount of
No of goods per arrival
13
13
13
13
No of trips per day
88
8
8
goods coming andInterarrival distribution
exponential exponential
15 min30 minthe same number oftruck trip arrives. Model 2 represents the current operation system of the building
establishment having two permanent workers stationed at the docking area. As discussed in
the previous section, with regard to the interarrival distribution, the exponential distribution
best fit the data. Two other models, Model 1 and 2, are developed to present alternatives to thepresent system.
Utilization Percentage = Total Travel to Use Time + Total Time In Usage x 100
Total Scheduled Time(1)

Equation 1 shows the computation process of utilization percentage (% utilization) of theworkers. This is define as the percentage of time the resource spent traveling to be used,transporting or processing an entity, or servicing a location or other resource. Utilization
percentage shows the total time of the workers involve in processing the goods. This value isthe reflection of real time the workers work. The three (3) variables composing the equation
are defined below:
Total Travel to Use Time is the time where the resource (workers) spent traveling to alocation (floors, docking are, etc) to transport or process an entity (goods), or to service a
location or other resource. It does not include any pick up time, but does include any blocked
time.
Total Time in Usage is the total time the resource spent transporting or processing and entity(goods), or servicing a location or other resource. Includes any pick up and drop-off time aswell as any blocked time while in use.
Total Schedule Time is the total number of hours the resources (workers) was schedules to beavailable.
The 100 multiplier in the equation is just a decimal conversion multiplier to make thepercentage figure a whole number rather than a pure decimal value.
4.5 Results of the models
After running the simulation, the models’ results shown in Table 14 were generated. The firstvariable means the average total time each goods spends in the system. This refers to themoment the goods arrived to the docking area (B3) until it was successfully transported to thereceiver. The total time spend by the goods to the simulations is higher (61.82) in Model 1.
This can be attributed to the assigning of one worker to facilitate the transport of goods. In
contrast, Model 2 drastically reduced the time spent by the goods to the system due to thepresence of two workers facilitating the goods. In this model, the two workers attended
several delivery trucks since the interarrival is short before transporting the consolidated
goods to the receivers. This model is behaving exactly the way the present system works
wherein as shown in the result of the survey the interarrival between trips is very short. Since
Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

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Table 14. Results of built models
Variables

Model 1Model 2Model 3Model 4

Ave min in system
Ave min in move logic
Ave min wait for pick up
Ave min blocked% utilization

61.82
27.49
5.8
28
75

36.73
18.99
4.26
13
57 & 57

52.12
25.8
3.58
5.58
37 & 38

20.25
14.27
2.84
3.12
42 & 46

the intervals between trips in Model 3 are 15 minutes, the two workers are transporting thegoods to the different recipients every time the delivery truck arrives. However, simulation
result shows that it took more than 15 minutes to transport 13 pieces of goods to the recipients.
This can be the reason why there is a long average time for the goods into the system (52.12).
Model 4 is characterized by 30 minutes interarrival times of truck trips, which is more thanenough time for the two workers to transport the 13 pieces of goods to the recipients andreturn to the docking area before another delivery truck arrives.
The second variable is the average time the goods spend traveling between floors and otherlocations, including any delays incurred. The results of Model 1 and Model 2 demonstrated
the advantage of having two workers as the move logic of goods decreases significantly.
The third variable is the average time the goods spend waiting for pick up or waiting foranother goods for consolidation before shipping. As the interarrival times of truck trips
increases, the waiting time for resources (workers) decreases. This result is not surprisingsince when the intervals between trips are longer, the two workers have enough time to return
to docking area after transporting the previous trip before another trip will arrive.
The fourth variable is the average time the goods spends waiting for the destination locations
to have available space. The destination locations in the model were the docking area and thefloors of the building. As the time intervals between trips increases, the less blocked time
showed by the model. Model 1 have a high blockade time which demonstrated the inability ofthe single worker to free up the docking area and other locations before another truck trip iscoming.
The utilization percentage, fifth variable of the model, of Model 1 is higher than of any othermodel. Model 2 and other models except Model 1 tend to suggest that the two workers areunder utilized. The present system adopted by the management of the building establishment
represented by Model 2 shows that the number of goods arriving and the number of truck
trips are below to what the two workers might capable of delivering.
4.5.1 How long does it really take to deliver one truck trip?
A single truck trip is simulated carrying only 13 goods in order to observe the delivery timeto the different floors using a) one worker and b) two workers. The difference between
running the simulation having only 13 goods (1 truck trip) from running it having many goods
and many truck trips is the former has no delay and therefore actual time to deliver all 13
goods can be observe. The worker/s usually consolidated the goods of several delivery trucks
at the docking are before shipping it to the recipients. In this manner, it is not possible toobserve the total time consumed for one truck trip.

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

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The simulation result for the first case (one worker only) shows that it took about 26 minutes
and 44 seconds from the moment the goods arrived before finally delivered to the receivers.
On the other hand, if the two workers are working together, it took them about 15 minutes and
40 seconds to dispatch the 13 goods. These findings are suggesting that if the management
decided to employ one worker, it would be appropriate to have an interval between trips ofgreater than the 26.44 minutes required to transport one trip of truck. In this manner, themoment another delivery truck arrives; the worker is already at the docking area. This willallow the delivery truck to move to another building without further delay. For the case of twoworkers assigned to transport the goods to the recipients, a higher than 15 minutes and 40
seconds will be suitable in order to attend the arriving delivery trucks immediately.
5. CONCLUSION
This study on a very simple elevator network used to transport goods gives several insights.
First, it was proven that the new surveillance device system called PEAMON, aside formonitoring outdoor travel behavior, could also be applied in monitoring worker’s activities
inside a commercial or manufacturing building. This finding provided an alternative to theconventional way of data gathering which usually done by number of people equipped withtimer. PEAMON data is free from human error or influence and therefore could provide moreaccurate. In most cases, data accuracy is a big factor to the outcome of a particular study.
Second, the present system employed by the management of the building establishment,
which hired two workers, seems under utilized. Terminating one worker, however, would
prolong the delivery time and cause long queue of goods since this single worker could hardly
attend to the very short time between arrivals. The early morning saw the arrivals of several
truck vehicles that are not observing the 15 minutes time interval between trips. Distributing
the arrivals of trucks to more long time, say 30 minutes would mitigate this problem however;this is very unlikely since most of the companies are rushing to move their supplies to theoffice in the early morning.
One way to increase the utilization percentage of the workers is to increase the frequency oftruck trips and the volume of goods. It is important, however, to emphasize to these delivery
trucks to observe a time pattern to avoid unnecessary delay that might be cause by theunscheduled trips. Increasing the frequency of trips would require the management of thebuilding to convince all the tenants to participate to the delivery cooperative system.
Simulation results show that even doubling the arrival volume of goods (13 at present) andvehicle trips (average is eight trips per day), the utilization percentage of these two workers
would still stand at 70 and 72.
This paper is only part of Marunouchi study, which comprises several topics. While somesignificant insights were revealed, the paper is far from over. Included in the future activities
to improve further this work is to calculate the travel time of goods from stock point to thedocking area of the building/s and from the docking area to each destination floor of goods.
The researchers also intend to include in the future work the effect of number of elevators to
the movement of goods. This will naturally involves the experimentation of adding building
floors and determining the appropriate number and properties of elevators.

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ACKNOWLEDGMENT
The authors would like to extend their gratitude to Ministry of Land, Infrastructure andTransport for having us the data of Marunouchi. Appreciation also goes to Mr. Yoshitaka
Komatsu, a master student for extending help in coding ProModel and Prof Saburo Tsuruta
for his valuable advice on the same software.
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Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003