Topic: Shipping
Sub- Topic: Maritime Security

RISK ASSESSMENT OF BLOCKADE OF THE MALACCA STRAIT USINGINTERNATIONAL CARGO SIMULATION MODEL
Ryuichi SHIBASAKI1
D. Eng., Senior Researcher, Port and Harbor Department,
National Institute for Land and Infrastructure Management,
Ministry of Land, Infrastructure, Transport and Tourism, Japan
3-1-1 Nagase, Yokosuka, Kanagawa, 239-0826, Japan
Email address: shibasaki-r92y2@ysk.nilim.go.jp
Phone: +81-46-844-5028
Fax: +81-46-844-6029
Tomihiro WATANABE
Head of Port System Division, Port and Harbor Department,
National Institute for Land and Infrastructure Management,
Ministry of Land, Infrastructure, Transport and Tourism, Japan
3-1-1 Nagase, Yokosuka, Kanagawa, 239-0826, Japan
Email address: watanabe-t2w3@ysk.nilim.go.jp
Phone: +81-46-844-5028
Fax: +81-46-844-6029
Abstract:
Malacca Strait is very important for international maritime shipping, not only in East Asian countries but also of
all over the world. If the Strait is blocked due to some reasons, the entire Asian economy will be thoroughly
affected, not only for international transport sectors. This paper aims to assess economic impact of the blockadeof the Malacca Strait quantitatively, by applying the simulation model the author had developed.
Until now, the author had developed the model to simulate international cargo flow including maritime, rail androad transport in East Asia. The model outputs international cargo flow by transport route on the land and sea,
when inputting the transport demand of international cargo from origins to destinations and status of transport
network. The author already validated the model accuracy to reproduce the actual international cargo flow tosome extent, and extended to incorporate all of the international land and hinterland transport network in East
Asia including Japan, Korea, China, and ASEAN countries, not only international maritime shipping network. In
this paper, using the model, several simulation results are shown and compared, based on the scenarios toassume the blockade of the Malacca Strait, especially from the viewpoints of how the transport pattern ofinternational cargo is changed in East Asia, how the cargo is shifted to other routes, how cargo throughput ineach Asian port is changed, and what international transport cost is increased.

Keywords: risk assessment, Malacca Strait, international cargo, transport cost, simulation model

1

Author for correspondence and presenting the paper

1Topic: Shipping
Sub- Topic: Maritime Security

RISK ASSESSMENT OF BLOCKADE OF THE MALACCA STRAIT USINGINTERNATIONAL CARGO SIMULATION MODEL
1. INTRODUCTION
Malacca Strait is very important for international maritime shipping, not only of East Asian
countries but also of all over the world. If the Strait is blocked due to some reasons, the entire
Asian economy will be thoroughly affected, not only for international transport sectors. The
considerable risks to block to pass through the Strait are, for example, natural disaster such as
tsunami, terrorism, unexpected huge accident, piracy. For example, Southeast Asia including
the Strait used to be the most dangerous area to suffer from piracy until recently. Anotherexample of risk is earthquake and tsunami. In the Indian Ocean Tsunami happened in 2004,
not only many people were killed, many port facilities were damaged in many countries inSoutheast Asia and South Asia, and needed long time to be restored.
This paper aims to assess economic impact of the blockade of the Malacca Strait
quantitatively, by applying the simulation model the author had developed. In this paper, theauthors focus on a direct impact what international shipping cost will increase due to theblockade of the Strait, although other economic impact such as the long-term impacts on
international/regional markets and governance systems can be considered. The one of reasons
why the authors focus only on the international shipping cost is that, according tomicroeconomic theory, a benefit or loss due to some transport policies and/or related events
can be calculated from the increased or decreased amount of international shipping cost if
equilibrium on international shipping market is realized, although it is difficult to assume themarket equilibrium under such a confused situation.
Until now, the authors (for example, Shibasaki et al., 2009; Shibasaki et al., 2010) had
developed the model to simulate international cargo flow including maritime, rail and roadtransport in East Asia, called the MICS (model for international cargo simulation). The model
can output international cargo flow by transport route on the land and sea, when inputting thetransport demand of international cargo from origins to destinations and status of transport
network. The author already validated the model accuracy to reproduce the actualinternational cargo flow to some extent, and extended to incorporate all of the international
land and hinterland transport network in East Asia including Japan, Korea, China, and
ASEAN countries, not only international maritime shipping network.
In this paper, using the model, several simulation results are shown and compared, based on
the scenarios to assume the blockade of the Malacca Strait, especially from the viewpoints of
how the transport pattern of international cargo is changed in East Asia, how the cargo isshifted to other routes, how cargo throughput in each Asian port is changed, and what
international transport cost is increased.
This paper is structured in the following way: Section 1 concerns introduction; Section 2
introduces the brief description of the model that the author had already developed; Section 3
describes the extension of the model to the ASEAN region; Section 4 expresses a few risk
scenarios, shows their simulation results, and discusses the implications derived from thesimulation results; and Section 5 address conclusions.

2Topic: Shipping
Sub- Topic: Maritime Security

2. MODEL STRUCTURE
2.1. General Outline
The model outputs transportation patterns of container cargo on maritime and land network,
given a regional cargo transport demand (OD cargo volume), service level of each port suchas the number of berths by water depth or port charges, and information related to thetransportation network (transportation costs and time etc.). The outputs are tabulated for eachport to calculate the handling volume and transshipment cargo volume by port.
The model focuses on the behavior of “shippers” and “ocean-going shipping companies” whoare the principal actors in the international container cargo shipping market. A “shipper”
makes reference to the freight and shipping time by route indicated by each ocean-going
carrier group, and selects a carrier group for maritime transport, ports used for import/export,
and land transport route and mode for each cargo. Here, the selection for the shipper isdetermined to minimize the “recognized generalized costs” including not only the shipping
cost and time, but also factors which cannot be observed by the model developer. Also, theselection is divided into two steps in the model; choice of carrier group and the others
including ports, land route and mode.
On the other hand, the “ocean-going shipping company”, which cargo transport demand isgiven as an input, assumingly behaves to maximize profit for each alliance (ocean-going
carrier group). Each group determines a freight and vessel size by port pair (combination of
port for export and import) and maritime transportation route such as ports of call andtransshipment ports, so that the profit (= income – costs) of their own group is maximized
with taking into account the behavior of other groups‟ freights and shipping time. Herein,
carrier groups assumingly behave within a shortsighted scope, although they consider
shipper‟s behavior as far as they can. Concretely, an ocean-going carrier behaves to maximize
its own profit in short term, considering shipper‟s behavior to select a carrier; however, thecarrier cannot predict a midterm behavior of shipper such as selection or change of port used
for export and import. In other words, each carrier group is assumed to have only short-term
strategy to compete with other carrier groups and deprive them of their cargo, not any
midterm strategy to encourage changing port for export and import for shippers. This
assumption reflects the actual situation of an international maritime container shipping market
in which change of freights or an entry and exit of carrier frequently occur and which carrier
often determines shipping routes through trial and error.
Summarizing the above discussion, in this research the authors develop two models as
described below; namely, a short-term model in which cargo shipping demands by port pair
are assumingly not changed, so that each carrier group determines freights by port pair tomaximize its own profit, reflecting behavior of other carrier groups and shipper‟s choice ofcarrier group. The other is a midterm model in which shipping demands by port pair can bechanged due to shipper‟s unrestricted choice of port used for export/import but shipping
demands by regional pair (i.e. a demand from a „true‟ origin to a „true‟ destination) are fixed.
In the midterm model, shippers and carrier groups are countervailed each other and neither ofthem has no power to control the international maritime container shipping market. Therefore,
the authors assume a Nash equilibrium to reach in which all shippers and all ocean-goingshipping companies cannot improve their own objective function when the behavior of theother party is not changed.
The following sections explain a profit maximization model (short-term model) of oceangoing carriers reflecting shippers‟ choice of carrier groups and an equilibrium model3

Topic: Shipping
Sub- Topic: Maritime Security(midterm model) between ocean-going carriers and shippers including shippers‟ choice ofport used for export/import separately.
2.2. Short-term Model – Profit Maximization Model of Ocean-going Carriers reflecting
Shippers’ Carrier Group Choice
2.2.1. Formulation
Ocean-going carriers determine freights by route in order to maximize their own profit, under
the condition that cargo transport demands by route are given. Herein, the authors assume thateach carrier group determines their freight with taking into account the freights and otherfactors on the level of service of the route for the other groups. In order to reflect the fact thatvarious elements other than the freight can be included in supplied transportation services, it
is assumed that all transport demand are not assigned to the group with the lowest freight on agiven route but rather some of them are assigned to groups with relatively high freights. In
other words, the model is a Bertrand equilibrium (price competition) model in an oligopolistic
market with product differentiation as an extraneous element to freights.
The profit maximization behavior by each carrier group is formulated asmax  g , g  G ,
p,x

(1)

s.t.  g    p ag  c ag   q ag  p a1 , , p ag , , p aG .

(2)

aA

The constraint condition (2) means that profit πg for each carrier group g is defined as the total
revenue minus total shipping cost of the group. Herein, pag: the freights (JPY/TEU) indicatedby a carrier group g on a maritime OD pair (combination of port used for import and export,
hereafter calling “port pair”) a, cag: the shipping cost (JPY/TEU) of carrier group g on port
pair a, qag: shipping volume of carrier group g on port pair a, A: set of port pairs, and G: set ofcarrier groups. Each carrier group g determines freights pag on a port pair a to maximize profit
making reference to the freights pag' of other groups g' ( g   G , g   g ) on the same port pairand carrier group selection behavior of shippers, given total demand da for each port pair. The
shipping volume qag of carrier group g on port pair a is assumingly formulated as thefollowing stochastic choice behavior of shippers, taking into account the factors which cannot
be observed by the model developer
qag  d a  probag ,s.t. probag 

exp    GM ag  exp    GM 

(3)
,

(4)

ag

gG

wherein probag: probability of carrier group g selection on a port pair a, θ: variance parameter,
and GMag: generalized cost of maritime shipping when using a carrier group g on a port pair a.
The shipper makes a selection based on service levels provided by each group (freights,
frequency, shipping time, etc.) and is not concerned with the shipping details such as theactual path and vessel size used which are determined by the carrier groups. The generalized
cost of maritime shipping GMag is formulated as
GM ag  pag  vtshpr  TM ag ,(5)
4

Topic: Shipping
Sub- Topic: Maritime Security

wherein vtshpr: value of time for shipper (JPY/TEU/hour), and TMag : total time of maritime
shipping (also including waiting time etc.) for carrier group g on a port pair a (hour).
2.2.2. Solution
As shown in Equation (3)-(5), the shipping volume qag does not depend on the freights pa’g of
any other port pairs a' ( a  A, a  a ) than that in the question and the shipping cost cag is
assumingly fixed in the short-term model. Therefore, Equation (1) and (2) are rewritten as thefollowing profit maximization by port pair

max pˆ ag  cag ,0 ， a  A, g  G ,s.t. pˆ ag  arg max pag  cag  qag  pa1 ,, pag ,, paG ,

(6)
(7)

p

wherein a function “max[x, y]” in Equation (6) means choosing larger one from x and y, and afunction “ arg max f  p  ” means choosing p to maximize f(p). These equations stand that thepfreight p̂ag is determined in order to maximize the profit for each port pair but if p̂ag is lower
than the shipping cost cag, it is defined as the same amount as cag supposing non-negative
profit.
Since the shipping cost cag does not depend on neither the shipping volume qag nor freights pag,when the freights of other groups are fixed ( pag  , g   g ), the first-order condition of
Equation (7) for each group g is written as p ag  q ag p a1 ,, p ag ,, p aG
p ag

  0 .

(8)

Therefore, when inputting Equation (3)-(5) into this, if probag  0 , it is acquired
probag 1
1  0 .
  p ag

(9)Equation (9) can be solved by using a quasi-Newton method.
2.3. Midterm Model – Equilibrium Model of Ocean-going Carriers and Shippers
considering Shippers’ Port Choice
2.3.1. Outline
In this model, given regional cargo transport demand, both ocean-going carriers and shippers
behave optimally each other according to respectively different objective functions in thecontext of a relationship of freights and shipping time by port pair and transport demand.
They are assumed to reach Nash equilibrium conditions that when the other actor‟s behavior
is given, a party cannot optimize their own objective function anymore.
Concretely, each shipper chooses a optimal shipping route including ports used forimport/export, making reference to the generalized cost GMag shown in Equation (5) indicatedby each ocean-going carrier group and other factors. On the other hand, each ocean-going
carrier group determines the freights for each port pair and transportation pattern, given the5Topic: Shipping
Sub- Topic: Maritime Security

shipping volume qag of carrier group g on port pair a acquired from the results of shippers‟
behavior as shown in the following sections.
2.3.2. Carrier Model: Formulation and Solution
In this midterm model, the profit maximization behavior for each carrier group is formulatedasmax  g , g  G ,

(10)

p,x

s.t.  g   pag  qag  pa1 , , pag , , paG    xvg  tvg x11,, xvg , , xVG ,aAqag   hakg , a  A ,

(11)

vV

and

(12)

kK agv
xvg     akg
hakg ,(13)aA kK ag

wherein xvg: container flow of link v (in the carriers‟ cost minimization model) for carriergroup g, tvg x,1 ,, xvg ,, xVG : shipping cost of link v for carrier group g per container (TEU),
V: set of links, hakg: shipping volume of containers on a path k in shipping demand qag ofv : Kronecker delta ( v =1; when a link v is included in the path k:group g on a port pair a,  akg
 akg
v = 0; when not included), and K : path choice set of shipping demand q .
 akg
ag
ag

The different point from the short-term model shown in Equation (1) and (2) is that eachshipping cost tvg depends on container flow xvg in the constraint condition (11). Note that thecost tvg is defined as generalized cost including shipping time; in other words, by considering
shipping time, notion of shippers is indirectly reflected in the cost minimization model ofcarriers. The other constraint condition (12) and (13) on cargo shipping demand qag andshipping amount xvg guarantee that all of cargo are transported.
Since Equation (10) cannot be solved by δπg/δx = 0 and δπg/δp = 0 due to difficulty ofdifferentiation, the above problem is solved by a following stepwise procedure; first, focusing
in minimization of total shipping costs expressed by the second term; and second, profit
maximization as shown in 2.2 when shipping cost in each port pair is fixed. Namely,
Step 0. n = 0; initial shipping demand qag(0)  by port pair for each carrier group is given,
estimated by shipping demand by region pair (which is expressed as Qrs  in next
section) and the share by ports of maritime container flow for each carrier group.
Step 1. n = n + 1.
Step 2. The cost minimization problem is solved under fixing shipping demand qag( n1)  for aprevious period as belown  n  n n 
n   ,min  xvg
 t vg x11 ,, xvg,, xVG

x
vV






g  G .

Step 3. The profit maximization problem is solved according to the solution described in
section 2.2 (short-term model) when shipping cost c ag(n)  in each port pair is fixed, by
calculating from the link costs tvg(n )  and cargo flow xvg(n)  determined in Step 2 as
below
n 
n 
 qagn   pan1 ,, pagn  ,, paGn  , g  G .
max   pag
 cag
p

aA

6

Topic: Shipping
Sub- Topic: Maritime Security

Note that when n = 1, the freights pag( n1)  in the previous iteration which are used in
the above calculation is substituted by the sum of monetary costs on the lowest-costroute using each link cost t vg(1)  which is obtained in Step 2.

Step 4. If the demand qagn1  for a previous period is converged comparing with the cargodemand qagn   from Step 3 to confirm convergence, or the repeat count n reaches anupper limit, the calculation is over. If not, return to Step 1.

In the above calculation procedure, the cost minimization problem stated in Step 2 isdescribed as a problem to determine a cargo flow of each link on international maritime
container shipping network as shown in Figure 1.
min  xvg  tvg x11,, xvg ,, xVG  , g  G .
x
vV


(14)

s.t. (12), (13)As shown in Figure 1, in this model, as links are set by vessel size, the decision problem of
each link flow includes the problem to determine, not only the handling volume for each port,
but also the vessel size transported. Equation (14) corresponds to a system optimum in a
traffic network equilibrium assignment methodology. Since the cost function tvg for each linkdepend not only on flow xvg for a given link but also on the flow xvg , v V of other links inthe same carrier group and the links xvg , g   G of other carrier groups, in this network
equilibrium assignment problem, interference from the flow of other links needs to beincorporated. For detail, please refer to Shibasaki et al (2005).
To port D

From port B
LargerContainership

LargerContainership

Port A
Arriving

DepartingAnchoring

SmallerContainership

SmallerContainership

CruiseCruiseUnloading

From port C

Transshipment

D nodeLoading

O nodeto Port E

Maritime link
Land link
Port link
Name of link

Figure 1. Network structure of carrier‟s cost minimization model (Source: Shibasaki et al.
2005)
2.3.3. Shipper Model: Formulation and Solution
In this model, cargoes are assigned on a network as shown in Figure 2. The maritime
transportation link is herein defined as the direct linkage between an export ports and an
import ports irrespective of the actual maritime transportation route and the shipping company
used which are considered in the carrier model. A stochastic (but not equilibrium) network
assignment model is also applied in this model taking into account factors which cannot be
observed by the model developer. Widely, a logit model is applied for this type of problem;
7

Topic: Shipping
Sub- Topic: Maritime Security

however, the authors do not apply due to the computational difficulties associated with thelarge number of choices in the logit model which requires enumeration of transportationroutes in advance in a large-scale network like this model or the expanded model discussed
below.
A shipper chooses a route (including mode of hinterland transport and port used forexport/import) to minimize expected generalized shipping costs, given freights for maritimeand land transport, the shipping time, etc. Herein, when Krs is path choice set of regionalcargo transport demand on a regional OD pair (hereafter calling “regional pair”) rs ( rs   ;Ω is set of regional pair), a cargo m chooses a path k to maximize utility Urskm including error
term εrskm, that is,
U rskm  U rskm , k  K rs , k   K rs , k  k , rs   ,s.t. U rskm  Grsk   rskm ,

(15)
(16)

wherein Grsk: shipping cost (JPY/TEU) of path k on a regional pair rs. If error term εrskm
follows Gumbel distribution, a choice of shipper is formulated asf rsk  Qrs 

exp    Grsk , exp    Grsk (17)kK rswherein frsk: cargo volume on a path k between regional pair rs, and Qrs: shipping demand(TEU) between regional pair rs. The shipping cost Grsk for each path is expressed by theequation below.
Grsk    a   GLb   GPX i  GPM i  GPTi  ,
ak

bk

(18)

ik

wherein Λa: the minimum expected cost (composite cost) for maritime links a including a pathk, which is a log-sum variable reflecting the selection result of carrier group as shown inEquation (3)-(5) in section 2.2. More precisely,a  

1 ln  exp    GM ag    ,

(19)

gG

wherein ζ: adjustment parameter to avoid the log-sum variable to be negative. GLb in
Equation (18) is the generalized shipping cost on land links b including the path k, expressedasGLb  CLb  vtshpr  TLb ,(20)

wherein CLb : freight on land link b (JPY/TEU), and TLb : shipping time (hours) on land linkb. Additionally, GPXi, GPMi, GPTi in Equation (18) are the cost of a port link i including thepath k. Figure 3 shows the network structure in each port which is omitted from Figure 2. As
shown in Figure 3, a receipt (of export cargo) and a dispatch (of import cargo) link arerespectively set to take account of the lead time in each port. In addition, an inter-carriertransshipment link is also considered for each port taking into account the transshipment
determined by the shipper. These link costs are defined as
8Topic: Shipping
Sub- Topic: Maritime Security

GPX i  vtshpr  TPX i ,(21)

GPM i  vtshpr  TPM i , and(22)

GPTi  CPTi  vtshpr  TPTi ,(23)

wherein, TPXi: lead time when export in port i (hours), TPMi: lead time when import in port i(hours), CPTi: freight when transshipped between carrier groups (JPY/TEU), and TPTi:
shipping time when transshipped between carrier groups (hours)．
Also, a relationship between the path flow frsk and the shipping demand da for each port pair isexpressed as a  f rsk , a  A ,d a     rsk(24)

rs kK rs

 a : Kronecker delta (  rsk
 a =1; when a link a is included in the path k on the regional
wherein  rsk
 a = 0; when not included).
pair rs:  rsk

As shown above, a stochastic network assignment model without any flow-independent link isapplied in this model. The cargo flow for each link is calculated using the Dial algorithm.
path

port
link

A

B

OriginDestinationrs
Maritime Link

C

DLand Link

Figure 2. Schematic view of network structure of shipper model (Source: Authors)land (export)

exportport A(receipt)
maritime (export)inter-carriertransshipment
land(import)

maritime (import)
import(dispatch)

Maritime Link
Land Link
Port Link

Figure 3. Network structure in port of shipper model (Source: Authors)
2.3.4. Procedure of Calculation to Acquire Nash Equilibrium Solution
Using initial conditions as a starting point and alternately repeating both shipper and carriermodel calculations, a local optimum solution is obtained according to following steps.
Step 0. [Setting initial condition] N = 0 and an initial calculation of profit maximization
problem of carrier groups is performed inputting initial values qag(0)  for shipping
demand and initial link cost tvg(0)  with respect to initial flow xvg(0)  by carrier group andusing the solution method described in section 2.3.2. In this manner, the shipping timeTM ag(0)  and freights pag(0)  by port pair for each carrier group are calculated.
9Topic: Shipping
Sub- Topic: Maritime Security

Step 1. N = N + 1.
Step 2. [Shipper model calculation] Based on the shipping time TM ag( N 1)  and freights p ag( N 1)  byport pair for carrier group calculated in the last step, calculation of the route choicemodel for shipper as shown in 2.3.3 is performed. In this manner, shipping demandd a( N )  by port pair for the overall carrier group is calculated.
Step 3. [Calculation of cargo shipping demand by carrier group] Cargo shipping demand q ag( N ) for each carrier group is calculated according to the short-term model shown in section
2.2, using freights p ag( N 1)  calculated in the previous iteration and total shipping
demand d a( N )  by port pair acquired in the previous step as initial values.
Step 4. [Carrier model calculation] Carrier model is calculated according to the solution
method described in section 2.3.2, using freights p ag( N 1)  calculated in the previous
iteration and shipping demand q ag( N )  by port pair for each carrier group calculated in

Step 3 as initial values. In this manner, the shipping time TM ag( N )  and the freights p ag( N ) by port pair for each carrier group, and the link flow xvg( N )  in the network of the cost
minimization model for carrier group can be obtained.
(N)
Step 5. [Convergence test] The sum XC ijsg
 of the four types of cruising link flow in the
network of the cost minimization model for carrier group calculated in Step 4 is( N 1)compared with the sum XC ijsg
 of the previously existing link flow and checked forconvergence. If it is converged or the repeat count N approaches an upper limit, theprocess is terminated, if not, return to Step 1.
3. MODEL EXTENSION AND INPUT DATA
3.1. Model Extension to ASEAN Countries
In order to do simulations for ASEAN Countries, a hinterland transport network in East Asia
except Japan needs to be incorporated in the above model. The network to be incorporated
includes roads, railways, and ferries as shown in Figure 4. The total length of such network ismore than 333,000 km, and it has 4,885 links in South East Asia alone. Some container ports
and ferry ports were also added, bringing the total to 33 container ports and 11 additional
ferry ports throughout the entire ASEAN region.
3.2. Data Preparation
Many types of input data need to be prepared for the model. They can be divided into fourtypes: 1) the transport demand of container cargo (OD cargo volume) by region; 2) transport
network data, such as the physical distance and operational costs of both international
maritime shipping and hinterland transport; 3) the service level at each port, such as thenumber of berths by depth, various fares and costs associated with berthing and sizes; and 4)
initial input, such as link flow between ports by ship size and by carrier and total volume ofcontainers handled by the ports. This model was basically developed based on year-2003 data,
for which the latest data are available, such as that of the Survey Report of International
Container Cargo Flow, conducted by the Japanese government every five years.
These four types of data are all difficult to obtain, especially the estimation of regional cargotransport demand. To make an OD cargo matrix on a regional basis, the cargo transportdemand on a country basis estimated with the trade model explained in Chapters 2 and 3 is10

Topic: Shipping
Sub- Topic: Maritime Security

proportionally divided in principle according to the share of magnitude of regional economy,such as GRP (gross regional products), based on statistics issued by each government (e.g.
Department of Statistics Malaysia, 2001, Statistical Yearbook Thailand 2004, SocioEconomic Statistical Data of 64 Provinces and Cities by Statistical Publishing House Ha Noi,
2005, and Statistical Yearbook of Indonesia, 2004).
The original model was developed under a network including 17 Japanese container ports, 8
Chinese ports, and 25 other Asian ports. There were four categories of containership size
(under 1000 TEU, 1000-2500 TEU, 2500-4000 TEU, and over 4000 TEU). The extended
model also targets the same Japanese ports and another 63 container ports in Asia including
ASEAN and China. Some input data related to the ports are shown in Table 1.
3.3. Confirmation of Model Accuracy
The reproducibility of the model in terms of the container cargo throughput in all Asian ports
is shown in the left side of Figure 5. As shown in the figure, predicted container cargothroughput was reproduced well by the developed model for most of the Asian ports. Whenfocusing on ASEAN ports, the reproducibility is fine, as shown in the right side of Figure 5.
Estimated international cargo flow on the land transport network is also shown in Figure 6.

Road
Railway
Domestic Shipping
Zone representative
Port

Figure 4. The hinterland transport network in ASEAN countries, incorporated into the model(Source: Authors)11Topic: Shipping
Sub- Topic: Maritime Security

Table 1. Model input data by port targeted (as of 2003)
Container Cargo Throughput***
Terminal
Port Charge***
Handl
Number of berth***(thousand TEU, excluding empty TransCharge Handling Time (hours)(thousand US$/vessel)-ing
containers)(million
shipCharge
No.
Port NameTransUS$/ment
1000- 2500(US$/4000
Export Import ship> -11- -13- <Trans Ratio*
year/
Total Export Import
TEU)* 1000 2500 4000 TEUped -11m -13m -14m -14m
-ship
berth)* Cargo CargoTEU TEUTEU
Cargo
1 JapanTomakomai
150 17.6
20.5
24.9 33.7
10.8
72
72
48
0
0
0
1
102
36
65
0
0%
2 JapanSendai-Shiogama
150 17.6
20.5
24.9 33.7
10.8
72
72
48
0
1
0
1
71
40
31
0
0%
3 JapanTokyo
150 17.6
20.5
24.9 33.7
10.8
72
72
48
3
2
0
10 2,527
914 1,304308
12%4 Japan
Yokohama
150 17.6
20.5
24.9 33.7
10.8
72
72
48
0
7
10
6 1,862
789
855
218
12%
5 JapanNiigata
150 17.6
20.5
24.9 33.7
10.8
72
72
48
4
1
0
0
82
20
62
0
0%
6 JapanShimizu
150 17.6
20.5
24.9 33.7
10.8
72
72
48
0
5
0
1
424
262
161
0
0%
7 Japan
Nagoya150 17.6
20.5
24.9 33.7
10.8
72
72
48
3
6
0
2 2,065 1,093973
00%
8 Japan
Yokkaichi
150 17.6
20.5
24.9 33.7
10.8
72
72
48
0
2
0
0
154
88
65
0
0%
9 Japan
Osaka
150 17.6
20.5
24.9 33.7
10.8
72
72
48
3
4
5
3 1,203
326
833
44
4%
10 Japan
Kobe
150 17.6
20.5
24.9 33.7
10.8
72
72
48
5
8
5
10 1,564
801
723
40
3%
11 JapanMizushima
150 17.6
20.5
24.9 33.7
10.8
72
72
48
5
0
0
0
70
38
32
0
0%
12 Japan
Hiroshima
150 17.6
20.5
24.9 33.7
10.8
72
72
48
7
0
0
1
111
71
40
0
0%
13 Japan
Tokuyama-Kudamatsu 150 17.6
20.5
24.9 33.7
10.8
72
72
48
0
1
0
0
70
50
20
0
0%
14 Japan
Hakata150 17.6
20.5
24.9 33.7
10.8
72
72
48
0
1
2
1
310
125
185
0
0%
15 Japan
Kita-Kyushu
150 17.6
20.5
24.9 33.7
10.8
72
72
48
3
4
0
0
317
172
145
0
0%
16 Japan
Shibushi
150 17.6
20.5
24.9 33.7
10.8
72
72
48
4
0
0
0
19
3
16
0
0%
17 JapanNaha
150 17.6
20.5
24.9 33.7
10.8
72
72
48
0
0
1
0
29
4
25
0
0%
18 South Korea
Busan
80
6.5
9.4
13.8 22.6
6.9
48
24
24
0
7
2
11 9,819 3,455 2,239 4,125
42%
19 South Korea
Kwangyang
80
6.5
9.4
13.8 22.6
6.9
48
24
24
0
0
0
6
768
466
302
0
0%
20 South Korea
Incheon80
6.5
9.4
13.8 22.6
6.9
72
48
60
0
3
0
2
758
460
298
0
0%
21 North Korea
Najin
20
6.5
9.4
13.8 22.5
5.0
312
432
276
1
0
0
0
197
47
150
0
0%
22 Far East Russia Vostochniy
20
7.5
10.4
14.8 23.5
5.0
72
48
60
0
2
2
0
618
532
86
0
0%
23 Far East Russia Vladivostok
20
7.5
10.4
14.8 23.5
5.0
72
48
60
0
2
0
0
215
185
30
0
0%
24 China
Dalian50
6.8
9.7
14.1 22.8
5.0
72
72
168
0
3
4
0 1,101
674
427
0
0%
25 China
Tianjin50
6.8
9.7
14.1 22.8
5.0
72
72
168
0
1
4
3 1,930 1,173757
00%26 China
Qingdao50
6.8
9.7
14.1 22.8
5.0
72
72
168
0
2
0
6 2,767 1,693 1,074
00%27 China
Lianyungang
50
6.8
9.7
14.1 22.8
5.0
72
72
168
0
2
0
0
213
130
83
0
0%
28 China
Shanghai50
6.8
9.7
14.1 22.8
5.0
72
72
168
7
6
5
4 7,397 4,526 2,871
00%29 China
Ningbo50
6.8
9.7
14.1 22.8
5.0
72
72
168
0
0
3
3 1,717 1,051667
00%30 China
Fuzhou50
6.8
9.7
14.1 22.8
5.0
72
72
168
2
3
0
1
525
321
204
0
0%
31 China
Xiamen50
6.8
9.7
14.1 22.8
5.0
72
72
168
0
2
3
0 1,645 1,006639
00%32 China
Shenzhen
50
6.8
9.7
14.1 22.8
5.0
72
72
168
0
2
0
10 5,494 3,361 2,133
00%33 China
Guangzhou
50
6.8
9.7
14.1 22.8
5.0
72
72
168
0
6
0
0 2,781 1,701 1,079
00%34 China
Zhongshan
50
6.8
9.7
14.1 22.8
5.0
72
72
168
4
0
0
0
524
321
204
0
0%
35 China
Fangcheng
50
6.8
9.7
14.1 22.8
5.0
72
72
168
0
2
0
0
33
20
13
0
0%
36 China
Haikou50
6.8
9.7
14.1 22.8
5.0
72
72
168
2
0
0
0
151
92
59
0
0%
37 China
Nanjing50
6.8
9.7
14.1 22.8
5.0
72
72
168
0
2
0
0
341
209
133
0
0%
38 China
Wuhan
50
6.8
9.7
14.1 22.8
5.0
72
72
168
6
0
0
0
99
61
39
0
0%
39 China
Chongqing
50
6.8
9.7
14.1 22.8
5.0
72
72
168
1
0
0
0
88
54
34
0
0%
40 Hong Kong
Hong Kong
100 15.8
18.7
23.1 31.8
22.6
24
24
12
0
0
0
23 14,649
378 1,452 12,819
88%
41 Chinese Taipei Keelung
60 12.4
15.3
19.7 28.4
8.7
48
48
48
0
14
0
0 1,800
817
928
55
3%
42 Chinese Taipei Taichung80 12.4
15.3
19.7 28.4
8.7
48
48
48
0
0
0
6
901
422
479
0
0%
43 Chinese Taipei Kaohsiung
50 12.4
15.3
19.7 28.4
8.7
48
48
48
4
5
0
10 7,222 2,360 2,681 2,18030%
44 Philippines
Subic Bay
30
6.5
9.5
13.9 22.6
5.0
72
96
84
0
3
0
0
51
18
33
0
0%
45 Philippines
Manila30
6.5
9.5
13.9 22.6
5.0
72
96
84
19
7
2
1 1,650
404
726
519
31%
46 Philippines
Batangas30
6.5
9.5
13.9 22.6
5.0
72
96
84
2
0
0
0
2
1
2
0
0%
47 Philippines
Cebu
30
6.5
9.5
13.9 22.6
5.0
72
96
84
4
0
0
0
391
140
251
0
0%
48 Philippines
Davao
30
6.5
9.5
13.9 22.6
5.0
72
96
84
4
0
0
0
40
14
26
0
0%
49 Vietnam
Haiphong
72
6.5
9.4
13.8 22.5
1.5
72
96
84
3
0
0
0
338
187
151
0
0%
50 Vietnam
Da Nang
72
6.5
9.4
13.8 22.5
1.5
72
96
84
5
4
0
0
30
16
13
0
0%
51 Vietnam
Ho Chi Minh City
72
6.5
9.4
13.8 22.5
1.5
72
96
84
10
6
2
0 1,321
729
592
0
0%
52 Cambodia
Phnom Penh
100
8.7
13.0
13.8 22.5
2.5
72
120
96
3
0
0
0
12
4
8
0
0%
53 Cambodia
Sihanoukville
100
5.8
9.4
13.8 22.5
2.5
72
120
96
1
0
0
0
230
76
154
0
0%
54 Thailand
Laem Chabang
84
7.3
10.2
14.6 23.4
1.6
72
48
60
0
0
0
7 2,703 1,708
881
114
4%
55 Thailand
Bangkok84
7.3
10.2
14.6 23.4
1.6
72
48
60
21
0
0
0 1,261
789
427
45
4%
56 Thailand
Songkhla
84
7.3
10.2
14.6 23.4
1.6
72
48
60
3
0
0
0
78
51
27
0
0%
57 Malaysia
Kuantan60
9.7
12.6
17.0 25.7
5.0
72
48
60
0
3
0
0
88
54
34
0
0%
58 Malaysia
Pasir Gudang
60
9.7
12.6
17.0 25.7
5.0
72
48
60
0
0
0
3
439
271
168
0
0%
59 Malaysia
Tanjung Pelepas
40
9.7
12.6
17.0 25.7
5.0
72
48
48
0
0
0
6 4,369122
75 4,17195%
60 Malaysia
Port Klang
60
9.7
12.6
17.0 25.7
5.0
72
48
60
0
0
0
19 7,713 2,223 1,377 4,113
53%
61 Malaysia
Penang60
9.7
12.6
17.0 25.7
5.0
72
48
60
2
3
0
0
402
249
154
0
0%
62 Malaysia
Kuching60
9.7
12.6
17.0 25.7
5.0
72
48
60
6
6
0
0
120
74
46
0
0%
63 Malaysia
Kota Kinabalu
60
9.7
12.6
17.0 25.7
5.0
72
48
60
3
0
0
0
156
96
60
0
0%
64 Singapore
Singapore70 10.1
13.0
17.4 26.2
6.2
24
24
12
6
15
4
18 13,607 1,566 1,020 11,021
81%
65 Myanmar
Thilawa
100
6.5
9.4
13.8 22.5
5.0
72
120
96
2
3
0
0
255
112
143
0
0%
Surabaya (Tanjung Perak)
66 Indonesia
50
7.7
10.7
15.0 23.8
5.0
48
144
96
11
0
0
0 1,314
845
470
0
0%
67 Indonesia
Semarang
50
7.7
10.7
15.0 23.8
5.0
48
144
96
2
0
0
0
373
240
133
0
0%
Jakarta (Tanjung Priok)
68 Indonesia
50
7.7
10.7
15.0 23.8
5.0
48
144
96
2
4
0
6 2,229 1,433797
00%69 Indonesia
Belawan50
7.7
10.7
15.0 23.8
5.0
48
144
96
5
0
0
0
245
158
88
0
0%
70 Indonesia
Pontianak
50
7.7
10.7
15.0 23.8
5.0
48
144
96
4
0
0
0
147
94
52
0
0%
71 Indonesia
Balikpapan
50
7.7
10.7
15.0 23.8
5.0
48
144
96
0
2
0
0
58
38
21
0
0%
72 Indonesia
Ujyung Pandang
50
7.7
10.7
15.0 23.8
5.0
48
144
96
0
2
0
0
197
127
70
0
0%
73 Brunei Darussalam Muara
20
6.5
9.4
13.8 22.5
5.0
48
24
36
0
2
0
0
193
104
89
0
0%
74 Bangladesh
Chittagong
20
6.5
9.4
13.8 22.5
5.0
120
96
108
2
0
0
0 1,249
256
993
0
0%
75 India
Jawaharlal Nehr
50
7.5
10.4
14.8 23.5
3.5
72
144
108
0
5
0
0 4,602 1,650 2,952
00%76 Sri Lanka
Colombo80
7.5
10.4
14.8 23.5
5.0
72
72
24
4
1
2
4 1,569256
227 1,086
69%
77 Pakistan
Karachi50
7.5
10.4
14.8 23.5
5.0
96
72
84
5
3
0
0 1,963374 1,589
00%78 Russia (European Area)
130 13.1
16.0
20.4 29.2
5.0
72
48
60
0
0
0 100# 2,867 1,488 1,379
00%#79 PNW
270 16.5
19.4
23.8 32.6
5.0
48
24
36
0
0
0 100 12,327 6,213 6,114
00%#
80 PSW
270 16.5
19.4
23.8 32.6
5.0
48
24
36
0
0
0 100 26,292 9,851 16,441
00%#81 East Coast of North America
270 16.5
19.4
23.8 32.6
5.0
48
24
36
0
0
0 100 16,062 6,877 9,185
00%#82 West Coast of South America
50
7.5
10.4
14.8 23.5
5.0
96
96
96
0
0
0 100
2,200 1,399
801
0
0%
#
83 East Coast of South America
50
7.5
10.4
14.8 23.5
5.0
72
96
84
0
0
0 100
5,813 4,004 1,80900%#Middle
East
84
50
7.5
10.4
14.8 23.5
5.0
48
24
36
0
0
0 100
9,521 5,844 3,678
00%85 Mediterranean
50
7.5
10.4
14.8 23.5
5.0
72
72
72
0
0
0 100# 15,027 6,344 8,683
00%86 Europe
130 13.1
16.0
20.4 29.2
5.0
24
24
24
0
0
0 100# 28,460 12,071 16,390
00%87 Africa
130 13.1
16.0
20.4 29.2
5.0
216
336
276
0
0
0 100# 5,813 4,144 1,669
00%
88 Oceania
50
6.5
9.4
13.8 22.5
5.0
24
72
48
0
0
0 100# 4,523 3,026 1,498
00%Source: * authors' estimation. ** World Bank: Doing Business. *** Containerisation International Yearbook etc.
Notice: # hypothetical assumption that there are no port congestions at all in non-Asian ports.

12Topic: Shipping
Sub- Topic: Maritime Security

All Asian ports
16

ASEAN ports

Estimated Throughput (mil. TEU)
Singapore

16

Estimated Throughput (mil. TEU)
Singapore

14

14
R² = 0.9003

R² = 0.9669

Busan

12

12

10

Shenzhen
8
Guangzhou

Hong Kong

Shanghai

8

Klang
Kaohsiung
TJ Pelepas

6

Keelung
4

10

TJ Pelepas
4

2

2

0

0
0

2

4

6

8

10

Klang

6

12

Actual Throughput

14

16

Laem Chabang

Jakarta (Tanjung Priok)
0(mil. TEU)

2

4

6

8

10

Actual Throughput

12

14

16

(mil. TEU)

Figure 5. Reproducibility of the model in terms of container cargo throughput(Sum of import, export and transshipped containers; in 2003) (Source: Authors)zone representatives
ports (No. of trucks/year)

Figure 6. Estimated international cargo flow in the land transport network (in 2003) (Source:
Authors)13

Topic: Shipping
Sub- Topic: Maritime Security

4. RISK SIMULATION USING THE MODEL4.1. Scenario Setting
Due to some reason, the Malacca Strait assumes to be blocked. Here, the blockade assumingly
continues enough long to change the transport pattern for the carrier groups and the transportroute and carriers for the shippers if needed. The carrier groups should change the shipping
route of containerships which passed through the Malacca Strait until the blockade. According
to our interview surveys, the alternative route is to pass through the Lombok Strait, which islocated between Bali and Lombok Island, Indonesia (see Figure 7). Another alternative route,
the Sunda Strait between Sumatra and Java Island, is not appropriate because it is too narrowand shallow to pass through for large ships.
The shutdown of the Malacca Strait is assumingly happened on a straight line between port of
TJ Pelepas (Malaysia) and Dumai (Indonesia). Any containerships and ferries passing throughthe line have to detour. For the model calculation, we have to set up new network data ofmaritime shipping on the physical distance between related port pairs. Concretely, thephysical distance of the maritime link connecting the west side (e.g. South Asia, Middle East,
Europe and Africa) and the east side (e.g. East part of Southeast Asia, East Asia, North &South America) of the blockade line are replaced in the blockade scenario.
Against the blockade, we prepare four countermeasure scenarios. For these additional
scenarios, the countermeasures implemented are focused on port of TJ Perak (Surabaya,
Indonesia). Although the scale of port of TJ Perak is now quite smaller, compared with
regional hub ports such as port of Singapore, it is located near the Lombok Strait andconsidered to have a potential to partly alternate the function of these hub ports. In this paper,
four policies to encourage the usage of port of TJ Perak in the emergency of the blockade of
the Malacca Strait are examined in order to alleviate the increase of international shipping
cost due to the blockade.

Klang
TJ Pelepas
Malacca Strait

Singapore
TJ Perark (Surabaya)

TJ Priok (Jakarta)
Sunda Strait (shallow)

Lombok Strait

Figure 7. Detouring route after the blockade of the Malacca Strait (Source: Authors)

14Topic: Shipping
Sub- Topic: Maritime Security

Another assumption is on the transport demand of international cargo of the world. If theblockade of the Strait actually happen and continue in a long time, the economy of the world,
not only Southeast Asia, will be thrown into utter chaos and demand of international shipping
will drastically decrease, at least in the meantime. That means the negative impact of theblockade is not limited to the increase of the shipping cost, but spread to the entire social
economy of the world. In the simulation of this paper, it is assumed that the transport demandof international cargo in any areas of the world will be not affected from the blockade and that
only the increase of shipping cost is negative impact to the world economy. This assumption
is mainly due to simplification of the calculation; therefore, the estimated cost increase should
be regarded as a minimum economic impact of the blockade.
4.2. Simulation Result of the Blockade
Figure 8 shows a comparison of the estimated volume of imported and exported container
cargo in Malaysian, Singapore and Indonesian ports between two scenarios. In Scenario 0, no
events are assumed to happen in 2003, which is the same result as shown in Figure 4 and 5. In
Scenario 1, the blockade is assumed to happen but any countermeasures are not implemented.
The estimated result shown in the figure is described as annual cargo volume. If the blockade
continues for half a year, the estimated cargo volume during this period will become half ineach scenario. In other words, for a discussion based on the result of the figure, the rate ofdifference for both scenarios is important; for example, in which ports, the increased or
decreased rate is relatively high due to the blockade, etc.
According to the result shown in Figure 8, in major ports in this area, port of Klang,
Singapore, and TJ Priok (Jakarta), the cargo volume handled for export/import is estimated todecrease due to the blockade. In particular, the decreased rate in port of Klang is the mostsignificant. On the other hand, especially in some Malaysian ports including Kuantan, Pasir
Gudang, and TJ Pelepas, the local cargo volume handled increase. These increased cargoes
are estimated to be sifted mainly from those handled in the port of Klang and Singapore.
Figure 9 shows a difference in land cargo flow in Southeast Asia. According to the figure, it isfound that the land link flows of both sides surrounding the blockade point are estimated toincrease. In Malay Peninsula, as discussed above, it is found that the container cargo handledin port of Klang shift to port of Kuantan, Pasir Gudamg, and TJ Pelepas. In addition, anothershift from sea to land is observed in the Sumatra Island, from a usage of port of Belawan toland transport to the Java Island across the Sumatra Island.
The result shown in Figure 10 bears out the above inference. Figure 10 shows the estimatedvolume of ferry/RORO cargo in Malaysian, Singapore and Indonesian ports. According to thefigure, in the ports which are located in the west of the blockade point such as port of Penang,
Dumai, and Belawan, the cargo volume drastically decrease after the blockade. Instead ofthem, in the ports which are located just in the east of the blockade point such as port of TJ
Pelepas and Panjang, the increased rates in the volume are significantly larger.
Also, Figure 11 shows a comparison of the estimated volume of transhipped container cargo
in Asian major ports. Similar to the result shown in Figure 8, the volume in port of Klang isestimated to drastically decrease due to the blockade, while in most of other hub ports
including Singapore they are increase.

15

Topic: Shipping
Sub- Topic: Maritime Security(mil. TEU)

4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

Scenario 0 (without any events)
Scenario 1 (Blockade)Indonesia

Malaysia

Figure 8. Comparison in estimated volume of local (imported and exported) container cargo
in Malaysian, Singapore and Indonesian ports (Source: Authors)

Songkla (Thailand)
Kota Kinabalu

PenangMuara
KemamanKuantanBelawanBintulu

Klang

Sibu

Dumai

Kuching
Pasir Gudang
Singapore
TJ Pelepas
Pontianak

portContainer/Ferry port

port

Ferry port

Palembang

Increased volume (unit/year)
Banjarmasin
Panjang
TJ Priok (Jakarta)
Semarang

TJ Perak (Surabaya)

Figure 9. Difference in land cargo flow in Southeast Asia between two scenarios (Source:
Authors)16

Topic: Shipping
Sub- Topic: Maritime Security

1.0(mil. Unit/year)

Scenario 0 (without any events)

0.8

Scenario 1 (blockade)

0.6
0.4
0.2
0.0

Indonesia

Malaysia

Figure 10. Comparison in estimated volume of ferry/RORO cargo in Malaysian, Singapore
and Indonesian ports (Source: Authors)(mil. TEU)

14.0
12.0
10.0

Scenario 0 (without any events)
Scenario 1 (Blockade)

8.0
6.0
4.0
2.0
0.0

Figure 11. Comparison in estimated volume of transhipped container cargo in Asian major
ports (Source: Authors)17

Topic: Shipping
Sub- Topic: Maritime Security

Summarizing the above results, port of Klang is predicted to be most heavily damaged fromthe blockade of the Malacca Strait. The reason can be explained as follows; if the Malacca
Strait is utterly shut down, Klang will become an innermost port of a fictitious huge bay ofwhich the mouth faces to the west; therefore, it is difficult to keep the function as a hub port.
On the other hand, port of Singapore can sustain the function as a hub port to the east part ofSoutheast Asia, because the east side of the port is open to ocean such as South China Sea andJava Sea.
Utilizing the model developed, the shipping cost of international container cargo can be also
calculated. Table 2 shows the increased cost estimated by cargo country/region ofinternational container cargo shipping due to the blockade of the Strait of Malacca. These
results can be calculated from the difference of the cost in each scenario. From the table, it isfound that the effects of the blockade are mostly significant in Malaysia and Singapore but
widely spread over the world. In particular, not only in ASEAN countries, but also in westneighbour countries to the ASEAN such as India, Sri Lanka and Middle East, the increased
rates of the shipping cost are more than 2%. This implies that these countries are strongly
connected to Southeast Asia and heavily affected from the blockade because it is locatedexactly in between the two areas. On the other hand, even ASEAN countries, for example, in
Vietnam, Cambodia and Brunei, the shipping costs are estimated to decrease. These countries
are quite remote from the Strait among ASEAN countries and located along the detour route
shown in Figure 6. Actually, in these counties‟ ports the volume handled of local andtranshipped cargo increase but they are not quite large. However, it is notable that thecountries (or ports) under these conditions are not always to increase their handling volume
and decrease shipping cost, like Philippines and Indonesia.
Table 2. Estimated increased cost (per year) and rate of international container cargo shipping
due to the blockade of the Strait of Malacca (Source: Authors)- ASEAN countries
Philippines Vietnam Laos Cambodia Thailand Malaysia Singapore Myanmar Indonesia Brunei
Increased
Cost (mil.
US$/year
Rate

64
0.2%

-172

47

-1

1,220

8,453

5,943

72

2,014

-0.3% 2.1%

0.0%

1.3%

7.1%

8.5%

0.9%

2.0%

ASEAN
Total

-90 11,214
-1.9%

2.6%

- other countries/regions
Japan Korea China
Increased
Cost (mil.
US$/year
Rate

ChineseIndia
Taipei

Russia/Sri
North
South Middle
World
CentralEurope Oceania Others
Lanka
America America East
Total
Asia

-440

700 1,640

2,434 2,395

334

1,098

-530

-447 5,640

8,757

1,957

1,159 29,595

-0.2%

0.6%

1.9%

2.9%

0.7%

-0.1%

-0.2%

1.0%

2.0%

0.5%

0.3%

2.0%

2.8%

0.6%

4.3. Simulation Result on Countermeasures to the Blockade
Against the blockade, the following four countermeasure scenarios are prepared on improving
the level of service in port of TJ Perak (Surabaya);
1) Scenario 1A (hereafter, S1A): all of containerships which are used to call at port ofSingapore before the blockade are assumingly compelled to shift to port of TJ Priok
temporally2.
2

Concretely, the initial maritime link flow, which is one of the input data of the model, is differently set.
18

Topic: Shipping
Sub- Topic: Maritime Security

2) Scenario 1B (S1B): In order to enhance its function as a transshipment port, in addition to
S1A, it is assumed that two new deeper berths (over -14m depth) are newly constructed andthat handling time for transshipped cargo is decreased as short as that in port of Singapore (i.e.
decrease from 96 hours to 12 hours).
3) Scenario 1C (S1C): In order to enhance its function as a regional port, in addition to S1B,
handling time for import and export are also decreased as short as that in port of Singapore(from 48 and 144 hours respectively to 24 hours for both).
4) Scenario 1D (S1D): In order to deal with emergency, in addition to S1C, the terminal
charge is almost exempted (from 5 million US$/year/berth to 0.01 million US$).
Figure 12 and 13 show the estimated local and transshipped container cargo for each above
scenario respectively. According to these figures, there are observed some effects to cargo
shift, especially in S1C and S1D. The result shown in Figure 12 implies that some local cargo
will shift to port of TJ Perak from other Indonesian ports as well as port of Singapore partly,by implementing the decrease of handling time for local cargo (which is firstly added in s1C).
Also, the result shown in Figure 13 implies that some transshipped cargo will shift from portof Singapore, partly by forcing the mandatory temporal shift of containership (s1A) and more
additionally by constructing new berths and shortening handling time for transshipment cargo(s1B). Another observation is a recovery of transshipped cargo in port of Klang. It should beinterpreted due to the weakening of the function as hub port in Singapore, rather than its
enforcement in port of TJ Perak.
Table 3 shows the increased or decreased cost estimated by cargo country/region ofinternational container cargo shipping by the countermeasure, compared with the result in
Scenario 1. From the table, it is found that for the cargo in the most affected countries fromthe blockade (i.e. Malaysia and Singapore) and Indonesia itself, the effect of thecountermeasure is expected to be positive; however, in most of other countries/regions,shipping cost is estimated to increase. That implies the impact of the blockade can equalize
throughout the world, although the total shipping cost of the world is estimated to slightly
increase.
(mil. TEU)

4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

Scenario 0

Scenario 1

s1A

s1B

s1C

s1D

Indonesia

Malaysia

Figure 12. Comparison in estimated volume of local container cargo for each countermeasurescenario on port of TJ Perak (Surabaya) (Source: Authors)19Topic: Shipping
Sub- Topic: Maritime Security(mil. TEU)
14.0

Scenario 0
s1A
s1C

12.0
10.0

Scenario 1
s1B
s1D

8.0
6.0
4.0
2.0
0.0

Figure 13. Comparison in estimated volume of transshipment cargo for each countermeasurescenario on port of TJ Perak (Surabaya) (Source: Authors)
Table 3. Estimated increased (or decreased) cost and rate of international container cargoshipping due to the countermeasure (s1C) comparing with the result in Scenario 1 (Source:
Authors)- ASEAN countries
Philippines Vietnam Laos Cambodia Thailand Malaysia Singapore Myanmar Indonesia Brunei
Increased
Cost (mil.
US$/year
Rate

97
0.3%

34

-11

14

190

-1,275-188-19-1,8300.1% -0.5%

0.2%

0.2%

-1.0%

-0.2%

-0.2%

-1.8%

ASEAN
Total

31 -2,005
0.7%

-0.5%

- other countries/regions
Japan Korea China
Increased
Cost (mil.
US$/year
Rate

ChineseIndia
Taipei

Russia/Sri
North
South Middle
World
CentralEurope Oceania Others
Lanka
America America East
Total
Asia

538

517 2,563

481

274

-22

478

52

600

864

1,549

387

1,089

6,413

0.3%

0.4%

0.4%

0.2% -0.2%

0.3%

0.0%

0.3%

0.4%

0.2%

0.4%

0.5%

0.1%

0.4%

5. CONCLUSIONSThis paper aimed to assess the economic impact from the viewpoint of international transport
cost due to the blockade of the Malacca Strait quantitatively, by applying the simulation
model the author had developed. By using the model, based on the scenarios to assume theblockade of the Malacca Strait and the countermeasures especially focused in port of TJ Perak,
the results were compared and discussed in terms of the cargo volume handled (local
container, ferry/RORO ship cargo, and transhipped container) in each neighbour port, landcargo flow shifted from the maritime shipping, and total and by-country shipping cost.
Since the results introduced in the previous chapter are based on some trial simulations,further simulations are needed to validate the model accuracy for detail; however, it can
predict reasonable answers on which port is the most affected and in which countries the20Topic: Shipping
Sub- Topic: Maritime Security

shipping cost increase. From now on, the authors consider conducting more simulations onother types of countermeasures and find more effective one.
ACKNOWLEDGMENTS
This research was partly supported by Grant-in-Aid for Scientific Research (B) No. 20310084.
REFERENCES
Shibasaki, R., Ieda, H., and Kadono, T. (2005) Model Improvement of International Maritime
Container Cargo Flow and Policy Evaluation for International Logistics in Eastern Asia,
proceedings of the 1st International Conference on Transportation Logistics, Singapore.
Shibasaki, R., Watanabe, T., and Araki, D. (2009) How Model Accuracy is Improved by
Usage of Statistics? - An Example of International Freight Simulation Model in East Asia -,Proceedings of the Eastern Asia Society for Transportation Studies, Vol.7.
Shibasaki R., Watanabe, T. (2010) How International Cargo Flow will Change by Expansionof Panama Canal? - An Approach using the World Model for International Cargo Simulation -.
Proceedings of the 3rd International Conference on Transportation Logistics, Fukuoka, Japan.
(under reviewing)21