Optimizing the transportation of international container cargoes in Korea

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Optimizing the transportation ofinternational container cargoes inKoreaHwa-Joong Kim a , Young-Tae Chang a , Paul T.-W. Lee b , Sung-HoShin a & Min-Jeong Kim aa Graduate School of Logistics, Inha University , Koreab Department of Logistics and Shipping Management , KainanUniversity , TaiwanPublished online: 13 Feb 2008.

To cite this article: Hwa-Joong Kim , Young-Tae Chang , Paul T.-W. Lee , Sung-Ho Shin & Min-JeongKim (2008) Optimizing the transportation of international container cargoes in Korea, MaritimePolicy & Management: The flagship journal of international shipping and port research, 35:1,103-122, DOI: 10.1080/03088830701849084

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MARIT. POL. MGMT., FEBRUARY 2008VOL. 35, NO. 1, 103–122

Optimizing the transportation of internationalcontainer cargoes in Korea

HWA-JOONG KIM*y, YOUNG-TAE CHANGy, PAUL T.-W.

LEEz, SUNG-HO SHINy, and MIN-JEONG KIMy

yGraduate School of Logistics, Inha University, KoreazDepartment of Logistics and Shipping Management, KainanUniversity, Taiwan

This paper considers a multimodal transportation problem, which is the problemof determining the transportation flow, i.e. volume of container cargoes, and thetransportation mode in each trade route, for the objective of minimizing the sumof shipping and inland transportation costs. The problem takes account of tworestrictions: maximum cargo volumes capacitated at each seaport and maximumnumber of vehicles available at each transportation mode. To solve optimally theproblem, this paper employs a mixed integer programming, which is anoperations research technique. A case study is performed on the containercargo data in Korea and we draw several implications to improve efficiency in thetransportation of international trade cargoes in Korea.

1. Introduction

To survive in the fierce competitive world, Korean government has made manyefforts to build a logistic hub of the Northeast Asia. In spite of the effort, it is knownthat there is still great inefficiency in the transportation of international trade cargoes[1]. One of the reasons may be ill-balanced cargo flows, that is, most of these cargoeshave been handled at seaports of Busan and Kwangyang, far from Seoul andKyounggi province in which nearly 40% of Korea’s population lives. Therefore,cargo flows in the transportation network should be redesigned to improve efficiencyin the transportation of international trade cargoes in Korea.

In this paper, we consider a multimodal transportation problem (MTP) in Korea,which is the problem of determining the cargo flow quantity, i.e. volume of containercargoes, and the transportation mode in each trade route, while satisfying thedemand of cargoes in foreign seaports and Korean cities with the supply in Koreancities and foreign seaports, respectively, throughout a planning period. The objectiveof the problem is to minimize the total logistic costs, i.e. shipping and transportationcosts (more detailed definition for the costs is given later). The MTP can beconsidered as a special case of the well-known network design problem (see [2] formore details of the network design problem), in that the MTP determines the flowquantity in each trade route while satisfying the demand and supply. In the MTP,however, we should determine the transportation mode in each trade route and

*To whom correspondence should be addressed. e-mail: [email protected]

Maritime Policy & Management ISSN 0308–8839 print/ISSN 1464–5254 online � 2008 Taylor & Francishttp://www.tandf.co.uk/journalsDOI: 10.1080/03088830701849084

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consider two restrictions: maximum cargo volumes capacitated at each seaport and

maximum number of vehicles available at each transportation mode.There are several previous research articles closely related to the MTP. Note that

all of the previous research consider routing problem while the MTP determine the

transportation flow. Min [3] considers the problem of determining the transportation

route and mode (among truck, airplane and ocean-ship) while sending cargoes to a

destination located in an oversea country. The objective is to minimize the cost and

time, and risk factors. To solve the problem, he employs a goal programming model

subject to chance constraint needed to calculate the risk. Barnhart and Ratliff [4]

consider the problem of determining the minimum cost routing for each shipment

with the combination of truck and rail. The cost includes the transportation and

inventory holding costs. To solve the problem, they employ a shortest path and

weighted b-matching algorithms. More recently, Boardman et al. [5] consider the

problem of determining the transportation route and the combination of

transportation modes (truck, rail, air and barge) while minimizing cost and time.

To solve the problem, they suggest a sort of shortest path algorithm. Other related

problems are network design and multimodal network flow problems. The network

design problem has been widely considered in the literature and there is a variety of

its applications including transportation, telecommunication and power systems [6].

For literature review for applications, models and methods, see [2, 7, 8]. Since there is

huge number of existing literatures, this review deals with only outstanding articles

classified using suggested methods, which are benders decomposition [6], dual accent

[8], cutting plane [9] and Lagrangian relaxation [10]. The multimodal network flow

problem determines the transportation flow and mode. Crainic and Rousseau [11]

consider the problem for the objective of minimizing the operation and delay costs

and suggest an optimal algorithm based on decomposition and column generation.

Guelat et al. [12] suggest a heuristic algorithm for the problem with the objective of

total routing and transfer costs and perform a case study with Brazil transportation

network and after Crainic et al. [13] analyse the rail component considered by Guelat

et al. [12] and suggest a strategic modelling framework of rail freight transportation.

Drissi-Kaitouni [14] suggests several heuristics, which are improved versions of

Guelat et al.’s [12] heuristic algorithm by identifying some unnecessary time

consuming part in Guelat et al.’s [12]. Haghani and Oh [15] consider the disaster

relief management problem, which can be regarded as the multimodal network flow

problem with time windows. To solve the problem they suggest two solution

algorithms working well large sized problems. Nijkamp et al. [16] compare statistical

estimation modes: discrete choice models and the neural network model using the

data of European freight flows.In this paper, we employ a mixed integer programming model to solve the MTP,

which is an operations research technique and is commonly used to solve a problem

such as that considered in this paper. A case study is performed on the container

cargo data in Korea and test results are reported. Also, we draw several implications

to improve efficiency in the transportation of international trade cargoes in Korea.

The next section describes the MTP in more detail and section 3 employs a mixed

integer programming model. Then, a case study on the real data of international

container cargoes in Korea is performed and test results are summarized in section 4

and, finally, this paper is completed with concluding remarks as well as future

research directions.

104 H.-J. Kim et al.

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2. Problem description

This section begins with explaining the transportation network. Figure 1 shows anexample of the transportation network. In the figure, the dotted area represents seaand the shaded area represents the inland of Korea. In the network, each nodecorresponds to foreign seaports, domestic seaport, inland container depots (ICD)and domestic cities, e.g. nodes F1 and F2 are foreign seaports, D1 and D2 aredomestic seaports, I1 and I2 are ICDs, and C1 and C2 are domestic cities. Also, eacharrow represents transportation flow of cargoes, i.e. solid arrows represent importflows while dotted arrows represent export flows. In the figure, the numbers locatedat the most left side imply the supply and the demand amounts in foreign seaports,while those located at the most right side imply those at domestic cities. For example,5000 20-foot equivalent units (TEU) and 10 000 TEU are the number of suppliedcargoes and the number of the demanded cargoes in foreign seaport F1, respectively.The transportation in the country is done by trucks and trains in a direct way to adestination (cities or domestic seaports) or by the way of an ICD. When imported bythe way of an ICD, it is assumed that trains first and then trucks are used totransport cargoes to cities, while vice versa when exported by the way of an ICD,according to the real situation in Korea. Here, the flow between ICDs is assumed notto occur based on the real situation in Korea.

Now, the MTP can be described as follows: for a given transportation network,the problem is to determine the transportation quantity and the number of vehiclesof each transportation mode over one planning period while satisfying the demand ofcargoes in domestic cities and foreign seaports using the supply in foreign seaportsand domestic cities, respectively, for the objective of minimizing the sum of shippingand transportation costs. The shipping cost implies the total cost charged whiletransporting cargoes from foreign to domestic ports. The cost includes the inventoryholding and transit costs, and terminal handling charge, where the inventory holding

10000 TEU

20000 TEU

5000 TEU

15000 TEU

import flow

export flow

Foreignseaports

F2

F1

D2

D1

C2

Domestic cities

I1

ICDs

I2

15000 TEU

15000 TEU

10000 TEU

10000 TEU

Domesticseaports

C1

Figure 1. An example of the transportation network for container cargo flow.

Optimizing transportation of international container cargoes 105

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cost is the cost occurred while cargoes are held during the transportation, the transit

cost implies the cost incurred for transporting cargoes, and the terminal handling

charge is the cost occurred for the stevedoring service of cargoes at a domestic

seaport. Here, we do not take account of the terminal handling charge at foreign

seaports since we assume that the charge at foreign seaports is already given before

shipping cargoes from foreign seaports. (Note that this paper considers cargoes after

shipping from foreign seaports.) Second, the transportation cost implies the total

cost charged while transporting cargoes in the country, which includes the inventory

holding and transit costs, and the terminal handling charge (if and only if visiting

any ICD).This paper considers two restrictions: capacity restriction and vehicle restriction.

The capacity restriction implies that there is a limitation on the total cargo volume

that can be handled at each seaport. The vehicle restriction consists of two

restrictions that are different with respect to transportation mode types

(two transportation modes are considered, truck and train, which are main

transportation means in Korea). For truck, the restriction is given in the form of

the total available time of trucks, which is the total time of trucks (available at each

Korean seaport or Korean city) that can be operated during the planning period.

On the other hand, the restriction for train is given in the form of the maximum

number of trains, which is the total number of trains operated on each train line

during the planning period.Finally, other assumptions made in this paper are summarized as follows:

(a) every parameter used in this paper is given and deterministic; (b) one type of ship

is used while transporting cargoes from foreign seaports to domestic seaports;

(c) there is no limitation on the number of ships, i.e. all cargoes can be transported

from foreign seaports to domestic seaports; (d) while transporting cargoes, one type

of container is used, each transportation mode is fully loaded, and traffic congestion

never occurs; and (e) all transportation modes are in perfect state, i.e. they are not

out of order throughout the planning period.

3. A mathematical programming model

In this section, we present a mixed integer programming model to solve optimally

and represent the MTP. First, the notations used in the formulation are

summarized below. (Note that parameters and variables given below are for one

planning period.)Parameters:

I set of foreign seaportsJ set of domestic seaportsK set of domestic citiesT set of ICDsM set of transportation modes, i.e. {1, 2} where 1 and 2 represent truck and

train, respectivelycfij shipping cost per TEU between i2 I and j2 J, which is calculated as

cfij ¼ h � ttij þ ctij þ thcj


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where h is the inventory holding cost per unit time and container box,

ctij is the transit cost per TEU, ttij is the shipping time, and thcj is the

terminal handling charge per TEU

nm number of TEUs that can be carried by transportation mode m2Mtmjk transit time of transportation mode m2M required for operating between

j2 J[T and k2Kcdmjk transportation cost of transportation mode m2M between j and k, which

is calculated using

cd mjk ¼ h � t mjk þ c m

jk

� �� nm

if j 2 J and k 2 K, and j 2 K and k 2 T

or j 2 K and k 2 T

where c mjk are the transit cost of transportation mode m per vehicle

uj total available time of trucks at j2 J[K[Tvjk number of trains operated on train line j to k, where j2 J and k2K or j2 J

and k2Tsfi supply amount from foreign seaport i2 Isdk supply amount from city k2Kdfi demand amount at foreign seaport i2 Iddk demand amount at city k2Kaj capacity of domestic seaport j2 J

Decision variables:SIij import amount from foreign seaport i2 I to domestic seaport j2 JSEji export amount from domestic seaport j2 J to foreign seaport i2 IDIjk import amount from domestic seaport j2 J to city k2KDEkj export amount from city k to domestic seaport j2 JVImjk number of vehicles of transportation mode m2M to transport cargoes

from j to k, where j2 J and k2K, j2 J and k2T, or j2T and k2KVEm

kj number of vehicles of transportation mode m2M to transport cargoes

from k to j, where k2K and j2 J, k2K and j2T, or k2T and j2 JAIjk import amount transported directly from j2 J to k2KAEkj import amount transported directly from k2K to j2 JTIjtk import amount transported from j2 J to k2K by the way of t2TTEktj export amount transported from k2K to j2 J by the way of t2T

Now, the integer program is given below.

Minimize

Xi2I

Xj2J

cfij � SIij þ SEji

� �þXj2J

Xk2K

Xm2M

cd mjk � VI mjk þ VEm

kj

� �

þXj2J

Xt2T

cd2jt � VI2jt þ VE2tj

� �þXt2T

Xk2K

cd1tk � VI1tk þ VE1kt

� �


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subject toXj2J

SIij ¼ sfi for all i 2 I ð1Þ

Xj2J

DEkj ¼ sdk for all k 2 K ð2Þ

Xj2J

DIjk ¼ ddk for all k 2 K ð3Þ

Xj2J

SEji ¼ dfi for all i 2 I ð4Þ

Xi2I

SIij ¼Xk2K

DIjk for all j 2 J ð5Þ

Xk2K

DEkj ¼Xi2I

SEji for all j 2 J ð6Þ

Xi2I

SIij þ SEji

� �� aj for all j 2 J ð7Þ

DIjk ¼ AIjk þXt2T

TIjtk for all j 2 J and k 2 K ð8Þ

DEkj ¼ AEkj þXt2T

TEktj for all j 2 J and k 2 K ð9Þ

AIjk �Xm2M

nm � VImjk for all j 2 J and k 2 K ð10Þ

AEkj �Xm2M

nm � VEmkj for all j 2 J and k 2 K ð11Þ

Xk2K

TIjtk � n2 � VI2jt for all j 2 J and t 2 T ð12Þ

Xk2K

TEktj � n2 � VE2tj for all j 2 J and t 2 T ð13Þ

Xj2J

TIjtk � n1 � VI1tk for all t 2 T and k 2 K ð14Þ

Xj2J

TEktj � n1 � VE1kt for all t 2 T and k 2 K ð15Þ


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Xk2K

t1jk � VI1jk � uj for all j 2 J ð16Þ

Xj2J[T

t1jk � VE1kj � uk for all k 2 K ð17Þ

Xk2K

t1tk � VI1tk � ut for all t 2 T ð18Þ

VI2jk � vjk for all j 2 J and k 2 K [ T ð19Þ

VE2kj � vkj for all j 2 J and k 2 K ð20Þ

VE2tj � vtj for all j 2 J and t 2 T ð21Þ

SIij, SEji � 0 for all i 2 I and j 2 J ð22Þ

DIjk,DEkj � 0 for all j 2 J and k 2 K ð23Þ

VImjk, VEmkj � 0 and integers for all j 2 J, k 2 K, and m 2 M ð24Þ

VI1tk, VE1kt � 0 and integers for all t 2 T and k 2 K ð25Þ

VI2jt, VE2tj � 0 and integers for all j 2 J and t 2 T ð26Þ

AIjk, AEkj � 0 for all j 2 J and k 2 K ð27Þ

TIjtk, TEktj � 0 for all j 2 J, t 2 T, and k 2 K ð28Þ

The objective function denotes the sum of shipping and transportation costs.

Constraints (1)–(4) represent the supply and demand restrictions. In more detail,

constraints (1) and (2) represent the supply restrictions, which imply that the cargoes

going out from a foreign seaport and a domestic city should be equal to the supply

amount in the seaport and the city, respectively. On the other hand, constraints (3)

and (4) represent that demands in a foreign seaport and a domestic city should be

satisfied, respectively. Constraints (5) and (6) represent the flow conservation, which

implies that the amount of cargoes coming to a domestic seaport is equal to the

amount of cargoes going out from the seaport. Constraint (7) states that the total

amount of cargoes handled at a domestic seaport cannot exceed the capacity of the

seaport. Constraints (8) and (9) generate the amount of cargoes transported directly

or by way of an ICD. Constraints (10)–(15) generate the number of vehicles used to

transport cargoes, together with constraints (16)–(21) that restrict the number

of vehicles available at each depot. In more detail, the cargoes imported

(constraint (10)) and exported (constraint (11)) in a direct way, i.e. not by way of

an ICD, are transported by vehicles, i.e. truck and/or train. Constraints (12) and (13)


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state that cargoes are transported to the destination by way of an ICD by train, whiletrucks are used in constraints (14) and (15). Constraints (16)–(18) represent that thetotal time required for using trucks is less than or equal to their available time at eachdepot. Constraints (19)–(21) represent that the number of trains operated on atrain line cannot exceed the existing number of trains available for the line.Finally, the other constraints (22)–(28) are the conditions on the decision variables.In particular, the number of vehicles represented in constraints (24)–(26) shouldhave integer values.

4. A case study

In this section, a case study on the container cargo data in Korea is performed. First,the container cargo data in Korea is summarized and then, test results andimplications are summarized. In this test, CPLEX 9.1, a commercial softwarepackage, was used to solve the mixed integer program.

The data include two transportation modes (truck and train), two ICDs(Uiwang and Yangsan), five domestic ports (Busan, Gwanyang, Incheon, Ulsanand Pyeongteak), and 43 domestic cities (Gangwon 1, 2, 3, Gyunggi 1, 2, 3,Gyungnam 1, 2, 3, Gyungbuk 1, 2, 3, Gwangju 1, 2, 3, Daegu 1, 2, Daejon 1, 2,Busan 1, 2, 3, Seoul 1, 2, 3, 4, Ulsan 1, 2, Incheon 1, 2, 3, Cheonnam 1, 2, 3,Cheonbuk 1, 2, 3, Chungnam 1, 2, 3, and Chungbuk 1, 2, 3), which are major tradinggates in Korea. We consider only seaports in Northeast and Southeast Asia, sincethis paper focuses on seaports within short sea in the viewpoint of Korea, and theyare the seaports of Yokohama, Yamaguchi, Tokyo, Osaka, Nagoya, Hakata, Otherports in Japan, Hong Kong, Kaohsiung, Keelung, other ports in Taiwan, Shanghai,Xingang, Dalian, Qingdao, Ningbo, Weihai, Yantai, other ports in China,Singapore, Malaysia, other ports in Southeast Asia (22 foreign seaports areconsidered). Also, we aggregated cities according to an expert’s advice intransportation due to an excessive amount of cities in Korea. The number ofTEUs transported by a transportation mode was set to 1 for a truck and 50 for atrain and the number of TEUs by a ship was set to 1100 for Southeast Asiancountries, i.e. SP, ML, OS and 600 for the others. Also, the inventory holding costwas set to $0.42 using the method given in Chang and Sung [17]. Table 1 summarizesthe supply and demand data in foreign seaports and domestic cities in year 2005.Also, table 2 summarizes the capacity (Ci) of each domestic seaport, which iscalculated by

Ci ¼ ri � C�i

where ri is the ratio of the cargo volume corresponding to short sea shipping amongthe total throughput handled at domestic seaport i and Ci

* is the total capacity of theseaport obtained after eliminating the transshipment cargoes. Remaining data aregiven in the Appendix. Note that the available time of truck and the number of trainsare calculated by using the similar method used in calculating the capacity of eachdomestic seaport.

Test results are summarized in table 3, which shows the optimal solution, thethroughput at each domestic seaport and the number of vehicles of eachtransportation mode. As can be seen from table 3(a), the optimal throughput ofBusan seaport is less than its real throughput in year 2005. This result can be


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Table

1.

Supply

anddem

anddata

(TEU).

(a)Foreignseaport

YK

YM

TK

OS

NG

HT

OJ

HK

KS

KL

OT

Supply

109400

88351

84701

108046

62065

91710

199806

500401

45002

473842

11869

Dem

and

40207

27137

63486

53913

41723

24434

103842

264860

54847

60401

20618

SH

XG

DL

QD

NB

WH

YT

OC

SP

ML

OS

Supply

158062

176090

112332

182100

74394

36175

22378

88826

135545

107585

15703

Dem

and

182092

206449

93908

153088

71306

38333

49535

17041

68902

97937

353943

Notes:YK:Yokohama,YM:Yamaguchi,TK:Tokyo,OS:Osaka,NG:Nagoya,HT:Hakata,OJ:

Other

portsin

Japan,HK:HongKong,KS:Kaohsiung,KL:Keelung,

OT:Other

portsin

Taiwan,SH:Shanghai,XG:Xingang,DL:Dalian,QD:Qingdao,NB:Ningbo,WH:Weihai,YT:Yantai,OC:Other

portsin

China,SP:Singapore,

ML:Malaysia,OS:Other

portsin

Southeast

Asia.

Source:

KoreaCustomsandTradeDevelopmentInstitute.


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Table

1.

Continued.

(b)Domesticcity

GW1

GW2

GW3

GG1

GG2

GG3

GN1

GN2

GN3

GB1

GB2

Supply

230

2300

2933

37928

236253

23371

13892

57504

71583

18741

24337

Dem

and

1608

3179

1708

128875

759738

100390

14000

43918

55100

19572

39169

GB3

GJ1

GJ2

GJ3

DG1

DG2

DJ1

DJ2

BS1

BS2

BS3

Supply

93367

47345

7396

24354

17854

40374

3451

47828

13970

7368

13701

Dem

and

73812

8602

9550

61009

12379

29158

6520

93409

38109

65933

53642

SU1

SU2

SU3

SU4

US1

US2

IC1

IC2

IC3

CN1

CN2

Supply

99002

6186

64594

38199

34007

185912

1101

59231

155118

8833

2515

Dem

and

306381

23798

302355

160714

15370

27189

424

158420

50039

6258

3229

CN3

CB1

CB2

CB3

CUN1

CUN2

CUN3

CUB1

CUB2

CUB3

Supply

377182

55434

5745

1305

58379

31703

39357

45362

9750

3007

Dem

and

26678

28659

2107

3191

9898

16742

50114

51049

16855

5533

Notes:

GW1:Gangwon1,GW2:Gangwon2,GW3:Gangwon3,GG1:Gyunggi1,GG2:Gyunggi2,GG3:Gyunggi3,GN1:Gyungnam1,GN2:Gyungnam2,

GN3:Gyungnam3,GB1:Gyungbuk1,GB2:Gyungbuk2,GB3:Gyungbuk3,GJ1:Gwangju1,GJ2:Gwangju2,GJ3:Gwangju3,DG1:Daegu1,DG2:Daegu2,

DJ1:Daejon1,DJ2:Daejon2,BS1:Busan1,BS2:Busan2,BS3:Busan3,SU1:Seoul1,SU2:Seoul2,SU3:Seoul3,SU4:Seoul4,US1:Ulsan1,US2:Ulsan2,IC

1:Incheon1,

IC2:Incheon2,IC

3:Incheon3,CN1:Cheonnam1,CN2:Cheonnam2,CN3:Cheonnam3,CB1:Cheonbuk1,CB2:Cheonbuk2,CB3:Cheonbuk3,CUN1:Chungnam1,

CUN2:Chungnam2,CUN3:Chungnam3,CUB1:Chungbuk1,CUB2:Chungbuk2,CUB3:Chungbuk3

Source:

KoreaCustomsandTradeDevelopmentInstitute.


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translated as: Busan seaport should more focus on deep sea shipping; or a less

capacity of Busan seaport than the current capacity is required. On the contrary, the

optimal solution indicates that the other seaports should be fully used (more capacity

may be needed), in other words, the other seaports should be more used for handling

short sea shipping. From table 3(b), we can see that truck should be mainly used to

transport cargoes corresponding to short sea shipping. Note that although the

number of trains is 2903 in total and hence 145 150 TEU (¼2903 � 50) should be

carried by trains due to the assumption of full-loaded vehicle, we put the number of

TEUs of trains as 120 071 TEU in table 3(b), which is obtained by subtracting the

number of TEUs of trucks (4 852 314) from the total cargo volume for short sea

shipping traded in Korea (4 972 385 TEU).Table 4 shows the cargo volume for metropolitan cities in Korea (Seoul, Incheon,

Gyunggi province) handled at each seaport. Like the current situation in Korea,

Busan seaport should handle 70.5% of the cargo volume for Seoul (SU1-4), Incheon

(IC1-3) and Gyunggi province (GG1-3). Also, we can see that seaports of

Gwangyang and Pyeongteak should be rarely used for trading the cargoes generated

Table 2. Capacity of domestic seaport.

Port Capacity (TEU)

Busan 4 645 322Gwangyang 818 603Incheon 713 696Ulsan 183 859Pyeongteak 134 292

Source: Each Regional Ports Maritime Affairs and FisheriesOffice.

Table 3. Test result.

(a) Throughput at each domestic seaportTEU CP�� (%) Share�� (%)

Busan 3 121 935 (3 894 421)� 67.2 (83.8)� 62.8 (78.3)�

Gwangyang 818 603 (441 774) 100.0 (54.0) 16.5 (8.9)Incheon 713 696 (449 780) 100.0 (63.0) 14.4 (9.0)Ulsan 183 859 (85 672) 100.0 (46.6) 3.7 (1.7)Pyeongteak 134 292 (100 738) 100.0 (75.0) 2.7 (2.0)

Notes: �test result and real data in parenthesis.��throughput/capacity � 100.��throughput/total cargo volume � 100.

(b) Number of vehicles of each modeNV� TEU Share�� (%)

Truck 4 852 314 4 852 314 97.6Train 2903 120 071 2.4

Notes: �number of vehicles.��cargo volume/total cargo volume � 100.


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in metropolitan cities. An interesting point is that Incheon seaport should handleonly metropolitan cargoes as can be seen from tables 3 and 4.

5. Concluding remarks

In this paper, we considered the problem of determining the transportation flowquantity and the transportation mode in each trade route, for the objective ofminimizing the sum of shipping and transportation costs, restricted to maximumcargo volumes capacitated at each seaport and maximum number of vehiclesavailable at each transportation mode. To solve optimally and represent theproblem, this paper employed a mixed integer programming. From the case study onthe container cargo data in Korea, the optimal solution suggested that Busan seaportshould be less used for handling the cargo, while the seaports should be more used.Second, truck should be mainly to transport the cargo, and, finally, the cargo formetropolitan cities should be handled mainly Busan and Incheon seaports.

For further study, the various ship types will be considered to make this researchmore realistic. Third, the effect if transit costs become different will be analysed.Fourth, time factors will be considered in the objective function to consider the realsituation that trucks are more used than trains in Korea (note that time factors areconsidered only together with inventory holding costs in this paper).

There are some future research directions. First, it is worth considering moretransportation modes, such as airplane, barge, etc. Second, the demand in foreigncities (not that in foreign seaports as used in this paper) is worthwhile beingconsidered. In this case, the capacity of foreign seaports and all parameterscorresponding to inland transportation should be used. Third, traffic andenvironmental factors should be considered to present more realistic situations.

References1. CHANG, Y. T., 2003, Korea’s strategic plan to be northeast Asia’s logistics hub: towards

the pentaport approach. Korea Observer, 34, 437–460.2. MAGNANTI, T. L. and WONG, R. T., 1984, Network design and transportation planning:

models and algorithms. Transportation Science, 18, 1–55.3. MIN, H., 1991, International intermodal choices via chance-constrained goal

programming. Transportation Research Part A: General, 25, 351–362.4. BARNHART, C. and RATLIFF, H. D., 1993, Modeling intermodal routing. Journal of

Business Logistics, 14, 205–223.5. BOARDMAN, B. S., MALSTROM, E. M., BUTLER, D. P. and COLE, M. H., 1997, Computer

assisted routing of intermodal shipments. Computers and Industrial Engineering, 33,311–314.

Table 4. Cargo volume for metropolitan cities.

TEU Share (%)�

Busan 2 029 645 73.0Gwangyang 35 519 1.3Incheon 713 696 25.7Ulsan 600 0.02Pyeongteak 0 0

Note: �TEU/total cargo volume for the cities � 100.


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6. COSTA, A. M., 2005, A survey on benders decomposition applied to fixed-charge networkdesign problems. Computers and Operations Research, 32, 1429–1450.

7. MINOUX, M., 1989, Network synthesis and optimum network design problems: models,solution methods and applications. Networks, 19, 313–360.

8. BALAKRISHNAN, A., MAGNANTI, T. L. and WONG, R. T., 1989, A dual-ascent procedurefor large-scale uncapacitated network design. Operations Research, 37, 716–740.

9. BALAKRISHNAN, A., 1987, LP extreme points and cuts for the fixed-charge network designproblem. Mathematical Programming, 39, 263–284.

10. HOLMBERG, K. and YUAN, D., 1998, A Lagrangean approach to network designproblems. International Transactions in Operational Research, 5, 529–539.

11. CRAINIC, T. and ROUSSEAU, J., 1986, Multicommodity, multimode freight transportation:a general modelling and algorithmic framework for the service network design problem.Transportation Research Part B, 20, 225–242.

12. GUELAT, J., FLORIAN, M. and CRAINIC, T., 1990, A multimode multiproduct networkassignment model for strategic planning of freight flows. Transportation Science, 24,25–39.

13. CRAINIC, T., FLORIAN, M. and LEAL, J., 1990, A model for the strategic planningof national freight transportation by rail. Transportation Science, 24, l–24.

14. DRISSI-KAITOUNI, O., 1991, Solution approaches for multimode multiproduct assignmentproblems. Transportation Research Part B, 25, 317–327.

15. HAGHANI, A. and OH, S.-C., 1996, Formulation and solution of a multi-commodity,multi-modal network flow model for disaster relief operations. Transportation ResearchPart A, 30, 231–251.

16. NIJKAMP, P., REGGIANI, A. and TSANG, W. F., 2004, Comparative modelling ofinterregional transport flows: applications to multimodal European freight transport.European Journal of Operational Research, 155, 584–602.

17. CHANG, Y.-T. and SUNG, S.-K., 2002, Revisit to estimate the time cost of ships andcargoes. Journal of Korean Navigation and Port Research, 26, 383–390.


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Appendix

Table A1. Shipping time (hour) and transit cost ($).

Busan Gwangyang Incheon Ulsan Pyeongteak

YK 2.1(250)� 2.3(300) 3.3(350) 2.1(300) -(-)YM 0.4(300) -(-)�� -(-) -(-) -(-)TK 1.8(300) 2.0(330) 2.9(350) 1.9(330) 2.8(350)OS 1.0(250) 1.2(270) 2.1(330) 1.1(270) -(-)NG 1.5(250) 1.7(270) 2.5(330) 1.6(270) 2.5(350)HT 0.3(250) -(-) -(-) -(-) -(-)OJ 1.2(267) 1.4(293) 2.2(340) 1.2(293) -(-)HK 3.7(150) 3.6(150) 3.9(250) 3.8(150) 3.9(250)KS 1.7(400) 1.6(400) 1.8(450) 1.8(400) -(-)KL 1.4(400) 1.3(400) 1.4(450) 1.5(400) -(-)OT 1.5(400) 1.5(400) 1.6(450) 1.7(400) -(-)SH 1.5(200) 1.4(230) 1.6(350) 1.6(230) 1.5(400)XG 2.4(250) 2.3(280) 1.5(400) 2.5(280) 1.5(450)DL 1.8(230) 1.7(260) 0.9(300) 1.9(260) 0.9(400)QD 1.6(250) 1.5(250) 1.1(350) 1.7(250) 1.1(450)NB 1.6(250) 1.5(280) 1.6(310) 1.7(280) -(-)WH 1.5(320) 1.4(320) 0.7(420) 1.6(320) 0.8(420)YT 1.7(400) 1.6(400) 0.8(450) 1.8(400) -(-)OC 1.7(271) 1.6(289) 1.2(369) 1.8(290) 1.2(424)SP 8.0(250) 7.9(250) 8.2(300) 8.1(250) -(-)ML 2.1(250) 2.3(300) 3.3(350) 2.1(300) -(-)OS 0.4(300) -(-) -(-) -(-) -(-)

Notes: GW1: Gangwon1, GW2: Gangwon2, GW3: Gangwon3, GG1: Gyunggi1, GG2: Gyunggi2, GG3:Gyunggi3, GN1: Gyungnam1, GN2: Gyungnam2, GN3: Gyungnam3, GB1: Gyungbuk1, GB2:Gyungbuk2, GB3: Gyungbuk3, GJ1: Gwangju1, GJ2: Gwangju2, GJ3: Gwangju3, DG1: Daegu1,DG2: Daegu2, DJ1: Daejon1, DJ2: Daejon2, BS1: Busan1, BS2: Busan2, BS3: Busan3, SU1: Seoul1, SU2:Seoul2, SU3: Seoul3, SU4: Seoul4, US1: Ulsan1, US2: Ulsan2, IC1: Incheon1, IC2: Incheon2, IC3:Incheon3, CN1: Cheonnam1, CN2: Cheonnam2, CN3: Cheonnam3, CB1: Cheonbuk1, CB2: Cheonbuk2,CB3: Cheonbuk3, CUN1: Chungnam1, CUN2: Chungnam2, CUN3: Chungnam3, CUB1: Chungbuk1,CUB2: Chungbuk2, CUB3: Chungbuk3� Shipping time and transit cost in parenthesis.��Shipping line is not available.Source: Korea Logistics Network Corporation and www.netpas.com.


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Table

A2.

Transittimebetweenseaport

andcity

(day).

GW1

GW2

GW3

GG1

GG2

GG3

GN1

GN2

GN3

GB1

GB2

GB3

GJ1

GJ2

GJ3

Busan

0.26(-)�

0.33(0.50)

0.28(0.56)

0.24(-)

0.19(-)

0.19(0.51)

0.08(-)

0.05(0.24)

0.18(-)

0.08(-)

0.05(-)

0.08(0.31)

0.17(0.32)

0.15(-)

0.06(-)

Gwangyang

0.30(-��)

0.37(-)

0.33(-)

0.25(-)

0.20(-)

0.20(0.49)

0.06(-)

0.09(-)

0.11(-)

0.15(-)

0.14(-)

0.14(0.41)

0.09(0.30)

0.06(-)

0.12(-)

Incheon

0.18(-)

0.25(-)

0.26(-)

0.10(-)

0.18(-)

0.07(-)

0.18(-)

0.22(-)

0.22(-)

0.19(-)

0.23(-)

0.15(-)

0.19(-)

0.17(-)

0.12(-)

Ulsan

0.26(-)

0.33(-)

0.29(-)

0.24(-)

0.20(-)

0.20(0.52)

0.11(-)

0.07(-)

0.23(-)

0.08(-)

0.06(-)

0.11(0.30)

0.20(-)

0.17(-)

0.08(-)

Pyeongteak

0.18(-)

0.25(-)

0.26(-)

0.10(-)

0.24(-)

0.29(-)

0.14(-)

0.19(-)

0.19(-)

0.17(-)

0.20(-)

0.12(-)

0.16(-)

0.14(-)

0.14(-)

DG1

DG2

DJ1

DJ2

BS1

BS2

BS3

SU1

SU2

SU3

SU4

US1

US2

IC1

IC2

Busan

0.08(-)

0.14(0.41)

0.14(-)

0.12(-)

0.23(-)

0.06(-)

0.21(-)

0.21(-)

0.20(-)

0.21(-)

0.21(-)

0.24(0.23)

0.30(-)

0.27(-)

0.23(-)

Gwangyang

0.10(-)

0.13(0.40)

0.13(-)

0.10(-)

0.13(-)

0.10(0.27)

0.21(-)

0.20(-)

0.21(-)

0.20(-)

0.21(-)

0.21(-)

0.13(-)

0.26(-)

0.23(-)

Incheon

0.16(-)

0.10(-)

0.10(-)

0.25(-)

0.23(-)

0.18(-)

0.18(-)

0.24(-)

0.06(-)

0.18(-)

0.18(-)

0.20(-)

0.21(-)

0.07(-)

0.12(-)

Ulsan

0.08(-)

0.15(-)

0.15(-)

0.30(-)

0.05(-)

0.06(0.23)

0.22(-)

0.21(-)

0.21(-)

0.21(-)

0.22(-)

0.18(-)

0.07(-)

0.28(-)

0.24(-)

Pyeongteak

0.14(-)

0.07(-)

0.08(-)

0.19(-)

0.22(-)

0.20(-)

0.29(-)

0.06(-)

0.07(-)

0.29(-)

0.29(-)

0.18(-)

0.19(-)

0.10(-)

0.06(-)

IC3

CN1

CN2

CN3

CB1

CB2

CB3

CUN1

CUN2

CUN3

CUB1

CUB2

CUB3

Busan

0.21(-)

0.15(-)

0.22(-)

0.11(0.39)

0.19(0.46)

0.17(-)

0.12(-)

0.24(-)

0.19(0.50)

0.16(0.43)

0.17(0.45)

0.16(-)

0.06(-)

Gwangyang

0.21(-)

0.07(-)

0.14(-)

0.18(0.22)

0.11(0.31)

0.09(-)

0.08(-)

0.20(-)

0.18(0.48)

0.14(0.41)

0.16(0.40)

0.20(-)

0.16(-)

Incheon

0.06(-)

0.18(-)

0.18(-)

0.22(-)

0.13(-)

0.14(-)

0.18(-)

0.20(-)

0.09(-)

0.12(-)

0.11(-)

0.12(-)

0.22(-)

Ulsan

0.22(-)

0.18(-)

0.25(-)

0.14(0.29)

0.21(-)

0.20(-)

0.15(-)

0.25(-)

0.19(0.42)

0.16(-)

0.18(0.44)

0.17(-)

0.14(-)

Pyeongteak

0.24(-)

0.14(-))

0.15(-)

0.19(-)

0.10(-)

0.11(-)

0.15(-)

0.05(-)

0.24(-)

0.08(-)

0.08(-)

0.12(-)

0.20(-)

Notes:

GW1:Gangwon1,GW2:Gangwon2,GW3:Gangwon3,GG1:Gyunggi1,GG2:Gyunggi2,GG3:Gyunggi3,GN1:Gyungnam1,GN2:Gyungnam2,GN3:

Gyungnam3,GB1:Gyungbuk1,GB2:Gyungbuk2,GB3:Gyungbuk3,GJ1:Gwangju1,GJ2:Gwangju2,GJ3:Gwangju3,DG1:Daegu1,DG2:Daegu2,DJ1:Daejon1,DJ2:

Daejon2,BS1:Busan1,BS2:Busan2,BS3:Busan3,SU1:Seoul1,SU2:Seoul2,SU3:Seoul3,SU4:Seoul4,US1:Ulsan1,US2:Ulsan2,IC

1:Incheon1,IC

2:Incheon2,IC

3:

Incheon3,CN1:Cheonnam1,CN2:Cheonnam2,CN3:Cheonnam3,CB1:Cheonbuk1,CB2:Cheonbuk2,CB3:Cheonbuk3,CUN1:Chungnam1,CUN2:Chungnam2,

CUN3:Chungnam3,CUB1:Chungbuk1,CUB2:Chungbuk2,CUB3:Chungbuk3�Transittimeforatruck

andatrain

inparenthesis.

��Train

lineisnotavailable.

Source:

www.roadplus.co.krandKoreaTransport

database

(www.ktdb.go.kr).


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Table

A3.

Transitcost

betweenseaport

andcity

($).

GW1

GW2

GW3

GG1

GG2

GG3

GN1

GN2

GN3

GB1

GB2

GB3

GJ1

GJ2

GJ3

Busan

590.0(-)�

507.0(170.7)569.5(202.6)542.5(-)

504.5(-)

489.5(176.6)199.5(-)161.5(35.5)160.0(-)

247.5(-)

218.5(-)

274.5(74.5)

318.0(76.6)318.0(-)264.5(-)

Gwangyang

525.0(-)

703.5(-)

516.0(-)

492.0(-)

428.5(-)

451.5(167.8)312.5(-)236.0(-)

180.0(-)

332.5(-)

512.5(-)

387.0(125.6)152.0(67.4)152.0(-)243.0(-)

Incheon

243.0(-)

246.5(-)

307.0(-)

192.5(-)

167.0(-)

219.5(-)

336.0(-)361.0(-)

357.0(-)

355.0(-)

355.0(-)

315.5(-)

348.5(-)

348.5(-)341.5(-)

Ulsan

505.5(-)

448.5(-)

505.5(-)

467.0(-)

446.0(-)

463.0(181.9)161.5(-)161.5(-)

136.0(-)

225.5(-)

118.0(-)

118.0(68.9)

342.0(-)

342.0(-)177.5(-)

Pyeongteak

261.5(-)

231.5(-)

317.0(-)

202.5(-)

102.0(-)

167.5(-)

338.0(-)490.5(-)

429.5(-)

405.5(-)

394.5(-)

327.0(-)

335.0(-)

335.0(-)342.0(-)

DG1

DG2

DJ1

DJ2

PS1

PS2

PS3

SU1

SU2

SU3

SU4

US1

US2

IC1

IC2

Busan

243.0(-)

306.5(126.3)306.5(-)

89.0(-)

106.0(-)

70.0(-)

482.5(-)496.5(-)

496.5(-)

482.5(-)

482.5(-)

136.5(33.6)

154.0(-)

539.0(-)502.0(-)

Gwangyang

278.5(-)

264.5(117.5)264.5(-)

249.5(-)

249.5(-)

249.5(50.4)

428.5(-)441.0(-)

441.0(-)

428.5(-)

428.5(-)

272.0(65.6)

272.0(-)

467.5(-)467.5(-)

Incheon

332.5(-)

273.0(-)

273.0(-)

465.0(-)

465.0(-)

465.0(-)

98.0(-)100.0(-)

100.0(-)

98.0(-)

98.0(-)

361.0(-)

361.0(-)

65.0(-)

65.0(-)

Ulsan

177.5(-)

290.5(-)

290.5(-)

132.5(-)

100.5(-)

117.5(30.8)

458.0(-)465.0(-)

465.0(-)

458.0(-)

458.0(-)

64.0(-)

64.0(-)

475.5(-)475.5(-)

Pyeongteak

359.5(-)1

89.0(-)

189.0(-)

463.0(-)

476.5(-)

463.0(-)

135.0(-)146.5(-)

146.5(-)

135.0(-)

135.0(-)

431.0(-)

431.0(-)

121.5(-)121.5(-)

IC3

CN1

CN2

CN3

CB1

CB2

CB3

CUN1

CUN2

CUN3

CUB1

CUB2

CUB3

Busan

539.0(-)

358.5(-)

347.5(-)

302.0(112.5)365.5(153.7)313.0(-)

322.5(-)436.5(-)

345.0(170.7)344.5(135.2)324.5(147.3)357.0(-)

372.0(-)

Gwangyang

467.5(-)

146.5(-)

160.0(-)

170.5(25.1)

237.0(73.0)

194.5(-)

203.0(-)374.5(-)

363.5(161.9)282.0(126.3)266.5(119.3)290.5(-)

421.5(-)

Incheon

65.0(-)

385.5(-)

385.5(-)

385.5(-)

303.5(-)

303.5(-)

326.0(-)289.5(-)

272.0(-)

224.0(-)

308.5(-)

308.5(-)

290.0(-)

Ulsan

475.5(-)

341.0(-)

368.0(-)

269.0(62.9)

382.0(-)

368.5(-)

357.5(-)393.0(-)

314.5(130.7)312.5(-)

298.0(143.5)320.0(-)

298.0(-)

Pyeongteak

121.5(-)

348.0(-)

386.0(-)

405.5(-)

231.0(-)

287.0(-)

303.0(-)125.5(-)

124.0(-)

113.5(-)

247.5(-)

206.5(-)

203.5(-)

Notes:See

footnote

inTable

A2.

*Transitcost

foratruck

andatrain

inparenthesis.

Source:

KoreaTransport

database

(www.ktdb.go.kr).


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Table

A5.

Transittime(day)andcost

($)betweenIC

Dandcity

oftrain.

Uiwang

Yangsan

Busan

0.34(-)�

0.18(37.0)

Gwangyang

0.31(0.0)

0.24(47.0)

Incheon

0.19(229.0)

0.35(-)

Ulsan

0.33(-)

0.19(-)

Pyeongteak

0.19(211.0)

0.33(-)

Note:�Transittimeandcost

inparenthesis.

Source:

KoreaTransport

database

(www.ktdb.go.kr).

Table

A4.

Transittime(day)andcost

($)betweenIC

Dandseaport

oftruck.

GW1

GW2

GW3

GG1

GG2

GG3

GN1

GN2

GN3

GB1

GB2

GB3

GJ1

GJ2

GJ3

Uiwang

0.08(154.6)�

0.07(146.1)0.10(182.8)0.06(106.1)0.02(88.0)

0.04(137.5)0.13(-)

0.13(-)

0.17(-)

0.14(-)

0.17(-)

0.10(-)

0.13(-)

0.12(-)

0.13(-)

Yangsan

0.20(550.0)

0.13(467.8)0.24(529.3)0.19(502.6)0.16(463.7)0.16(449.2)0.06(158.2)0.09(120.0)0.01(120.0)0.06(206.2)0.19(176.5)0.09(233.5)0.13(277.9)0.12(277.4)0.05(223.2)

DG1

DG2

DJ1

DJ2

PS1

PS2

PS3

SU1

SU2

SU3

SU4

US1

US2

IC1

IC2

Uiwang

0.12(-)

0.07(-)

0.07(-)

0.16(-)

0.17(-)

0.17(-)

0.02(81.6)

0.02(81.6)

0.04(60.2)

0.02(73.0)

0.12(81.6)

0.16(-)

0.17(-)

0.05(124.7)0.02(116.2)

Yangsan

0.05(202.2)

0.10(266.0)0.10(266.0)0.01(48.4)

0.00(67.0)

0.02(28.9)

0.15(442.2)0.17(456.1)0.16(456.2)0.16(442.2)0.05(442.7)0.02(97.4)

0.03(165.4)0.21(499.0)0.18(462.1)

IC3

CN1

CN2

CN3

CB1

CB2

CB3

CUN1

CUN2

CUN3

CUB1

CUB2

CUB3

Uiwang

0.02(124.7)

0.14(102.9)0.15(102.9)0.15(102.9)0.08(-)

0.11(-)

0.13(-)

0.06(-)

0.08(-)

0.05(-)

0.07(-)

0.03(-)

0.07(-)

Yangsan

0.17(498.6)

0.15(318.2)0.25(307.2)0.07(261.6)0.14(375.3)0.15(272.2)0.10(281.5)0.19(396.6)0.19(304.5)0.12(303.9)0.13(284.4)0.16(316.7)0.12(332.9)

Notes:See

footnote

inTable

A1.

*Transittimeandcost

inparenthesis.

Sources:www.roadplus.co.krandMinistryofConstructionandTransportation(w

ww.m

oct.go.kr).


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Table

A6.

Available

timeoftruck

(day).

(a)Domesticseaport

Busan

Gwangyang

Incheon

Ulsan

Pyeongteak

276075

475364

4088609

897143

2975189

Source:

KoreaTruckingAssociation(w

ww.kta.or.kr).

(b)Domesticcity

GW1

GW2

GW3

GG1

GG2

GG3

GN1

GN2

GN3

GB1

GB2

GB3

GJ1

GJ2

GJ3

392256

401082

344100

3128700

3406082

3420230

679633

795415

982716

762705

735707

888222

372007

688719

4988085

DG1

DG2

DJ1

DJ2

PS1

PS2

PS3

SU1

SU2

SU3

SU4

US1

US2

IC1

IC2

343062

649909

471045

681580

1639894

1709077

690926

8945297

1126145

1592517

1398206

271282

616161

194571

2158574

IC3

CN1

CN2

CN3

CB1

CB2

CB3

CUN1

CUN2

CUN3

CUB1

CUB2

CUB3

633165

580336

497134

807616

1299298

172245

130579

296723

573067

720001

235847

37383

67367

Notes:




1:Incheon1,IC

2:Incheon2,IC

3:


CUN3:Chungnam3,CUB1:Chungbuk1,CUB2:Chungbuk2,CUB3:Chungbuk3.

Source:


ww.kta.or.kr).

(c)IC

DUiwang

Yangsan

23708100

8874946

Source:


ww.kta.or.kr).


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Table

A7.

Terminalhandlingcharge($).

Busan

Kwangyang

Incheon

Ulsan

Pyungtack

Uiwang

Yangsan

133.5

107.7

112.2

107.7

107.7

133.5

107.7

Sources:

Korea

Shipping

Gazette

(http://w

ww.ksg.co.kr)

and

Uiwang

Inland

Container

Terminal

(http://

www.kicd.co.kr).


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Table

A8.

Number

oftrains.

(a)Betweendomesticseaport

andcity

GW1

GW2

GW3

GG1

GG2

GG3

GN1

GN2

GN3

GB1

GB2

GB3

GJ1

GJ2

GJ3

Busan

04

80

01352

042

00

0123

59

00

Gwangyang

00

00

0360

00

00

03

24

00

Incheon

00

00

00

00

00

00

00

0Ulsan

00

00

08

00

00

051

00

0Pyeongteak

00

00

00

00

00

00

00

0

DG1

DG2

DJ1

DJ2

PS1

PS2

PS3

SU1

SU2

SU3

SU4

US1

US2

IC1

IC2

Busan

041

00

00

00

00

08

00

0Gwangyang

06

00

020

00

00

01

00

0Incheon

00

00

00

00

00

00

00

0Ulsan

00

00

096

00

00

00

00

0Pyeongteak

00

00

00

00

00

00

00

0

IC3

CN1

CN2

CN3

CB1

CB2

CB3

CUN1

CUN2

CUN3

CUB1

CUB2

CUB3

Busan

00

053

118

00

052

105

93

00

Gwangyang

00

02

137

00

011

39

17

00

Incheon

00

00

00

00

00

00

0Ulsan

00

01

00

00

10

10

0Pyeongteak

00

00

00

00

00

00

0

Notes:




1:Incheon1,IC

2:Incheon2,IC

3:


CUN3:Chungnam3,CUB1:Chungbuk1,CUB2:Chungbuk2,CUB3:Chungbuk3.

Source:

KoreaTransport

database

(www.ktdb.go.kr).

(b)BetweenIC

Danddomesticseaport

Busan

Gwangyang

Incheon

Ulsan

Pyeongteak

Uiwang

971

360

012

0Yangsan

182

22

032

0

Source:

KoreaTransport

Data

Base

(http://w

ww.ktdb.go.kr).


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Optimizing the transportation of international container cargoes in Korea

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