Transport Modeling and Mitigation Planning For

平成 24 年度修士論文

TRANSPORT MODELING AND MITIGATION PLANNING FOR DISRUPTED TRANSPORT NETWORK

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九州大学院工学府海洋システム工学専攻修士１年 PUTU HANGGA NAN PRAYOGA 指導教員篠田岳思教授

2012 MASTER COURSE GRADUATION THESIS

TRANSPORT MODELING AND MITIGATION PLANNING FOR DISRUPTED TRANSPORT NETWORK

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KYUSHU UNIVERSITY GRADUATE SCHOOL OF ENGINEERING DEPARTMENT OF MARINE SYSTEMS ENGINEERING AUTHOR : PUTU HANGGA NAN PRAYOGA SUPERVISOR : PROF. TAKESHI SHINODA

THESIS OUTLINE

TITLE

TRANSPORT MODELING AND MITIGATION PLANNING FOR DISRUPTED

TRANSPORT NETWORK

STUDENT NAME : PUTU HANGGA NAN PRAYOGA

NATIONALITY : INDONESIA

STATUS : MASTER COURSE STUDENT

INTERNATIONAL MASTER COURSE PROGRAM (G30)

DEPARTMENT OF MARINE SYSTEM ENGINEERING

GRADUATE SCHOOL OF ENGINEERING

KYUSHU UNIVERSITY

STUDENT ID : 2TE11450S

SUPERVISOR : Prof. TAKESHI SHINODA

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FOREWORD

his thesis was written for the author’s Master degree in Marine Systems

Engineering at Kyushu University. The research was a compilation of previous Tindependent research related to transport modeling, disaster mitigation and route optimization. The author began to have interest in the research field since undergraduate time back in 2007 and continuously examine the research trend after working in container shipping company as fleet supervisor. The intention of this thesis is to examine how to best implement existing method for simulating cargo distribution in a disrupted network post a disaster especially in developing countries where fast re-instatement of broken infrastructures are difficult to be realize. The presented problem and case for application of the model is still exist until the production of this thesis. The author hope to be able to contribute for decision making process in mitigating disaster impact related to container cargo transportation. Finally, this research is still far from perfection and there are still many rooms for future development and correction.

CopyFukuoka, August 2012 Putu Hangga Nan Prayoga

TABLE OF CONTENTS

TITLE ...... i

FOREWORD ...... ii

LIST OF TABLES ...... v

LIST OF FIGURES ...... vi

ABSTRACT ...... vii

1 INTRODUCTION ...... 1

1.1 LITERATURE REVIEW ...... 2

1.2 OVERVIEW OF HOT MUD FLOW DISASTER IN EAST JAVA (LUSI) ...... 3

1.3 OVERVIEW OF DISRUPTED TRANSPORT NETWORK POST LUSI ...... 5

1.3.1 Role of Port of Tanjung Perak in Container Transportation ...... 5

1.3.2 Current Situation of Transportation in Disrupted Network ...... 6

2 METHODOLOGY FOR DISASTER MITIGATION PLANNING ...... 9

2.1 PROBLEM OF LAND TRANSPORT-CENTRIC ...... 9 2.2 MITIGATION STEPSCopy FOR SOLVING DISRUPTION IN CONTAINER TRANSPORTATION: APPLIED IN LUSI DISASTER CASE ...... 11

3 MATHEMATICAl MODEL of PHYSICAL DISTRIBUTION ...... 15

3.1 MODELING ASSUMPTION ...... 16

3.2 INTERNAL AND EXTERNAL COST ...... 18

3.3 MATHEMATICAL MODEL ...... 20

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4 APPLICATION OF MITIGATION PLANNING AND TRANSPORT MODELING IN

LUSI DISASTER CASE ...... 24

4.1 SETTING OF CONDITION ...... 24

4.2 APPLIED MITIGATION STEPS ...... 24

4.3 MODELING RESULT ...... 29

4.4 ANALYSIS OF SOLUTION ...... 29

4.5 SENSITIVITY ANALYSIS ...... 34

5 CONCLUSION ...... 37

ACKNOWLEDGEMENTS ...... 38

REFERENCES ...... 39

APPENDIX A: MEASURED INFRASTRUCTURE AND DEMAND DATA OF

PROSPECTIVE CONSOLIDATION POINTS ...... 42

APPENDIX B: GENERATED TRANSPORT ROUTES ...... 43

APPENDIX C: EXAMPLE CASE TO MEASURE THE ALGORITHM ABILITY TO DELIVER OPTIMUM SOLUTIONCopy ...... 46

LIST OF TABLES

Table 1 Comparison of LUSI disaster with similar phenomena in the world ...... 4

Table 2 Mode of transport characteristic for container transportation ...... 10

Table 3 Forecasted containerized cargo that flow around examined network ...... 12

Table 4 Cluster result of consolidation point (Sea port) for LUSI mitigation ...... 13

Table 5 Notation of assumption used in modeling ...... 17

Table 6 Modal capacity assumption for intermodal means of transport ...... 17

Table 7 Internal and external cost structure for construction of total transport cost ...... 19

Table 8 Basic assumption for calculation of transport emission ...... 19

Table 9 Example of cargo movement in modeling solution ...... 28

Table 10 Average transport cost comparison between simulated conditions ...... 30

Table 11 Total cost of transport for modeling solution, condition before LUSI and post LUSI

...... 31

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LIST OF FIGURES

Fig. 1 LUSI disaster in Porong District, East Java, Indonesia ...... 4

Fig. 2 Port of Tanjung Perak as Main Gate for Cargo Distribution ...... 6

Fig. 3 Transport freight for container cargo prior to LUSI...... 7

Fig. 4 Transportation network condition after LUSI disaster ...... 8

Fig. 5 Examined bottleneck in container transportation post LUSI ...... 8

Fig. 6 Benefit of modal shift in intermodal transportation system ...... 10

Fig. 7 Cluster concept of consolidation point ...... 13

Fig. 8 Illustration of consolidation point along disrupted network ...... 13

Fig. 9 Example of generated routes in intermodal transport for LUSI disaster mitigation ..... 14

Fig. 10 Concept of intermodal connection in disaster mitigation of cargo transportation ...... 15

Fig. 11 Illustrated model of route computation by proposed mathematical model ...... 23

Fig. 12 Mitigation framework for transport disruption in LUSI disaster case ...... 25

Fig. 13 Simplified scheme of intermodal network and example of routes calculation ...... 27

Fig. 14 Illustration of network solution from computational simulation ...... 28

Fig. 15 Dependence of transport cost (a) and unit cost (b) on door-to-door distances ...... 33 Fig. 16 Dependence between increasedCopy demand and transport cost ...... 34 Fig. 17 Dependence between increased demand and projected CO2 emission ...... 35

Fig. 18 Dependence between increased demand and utilization of sea port as consolidation point...... 36

ABSTRACT

LUSI disaster has been continuously creating serious damage to transportation infrastructure in East Java province of Indonesia since 2006. Mitigation of the unexpected event upon a disaster in the form of the worst case scenario that may bring adverse impact to transportation network has become a challenge for decision maker. In reality, emergency transportation system for the aim of compensation for disrupted network often tends to be far from efficiency. In this study, transport model aiming for disaster mitigation is introduced and is applied to real case.

Modeling relies on intermodal network utilization and internalization of CO2 emission as external impact because of production of transportation. Mainly, we examine how marine based mode of transport can help to mitigate land based disaster and how the solution gives impacts in effort to mitigate global warming issues.

Keywords: Container Transportation, Disaster Mitigation, Transport Modeling, Intermodal,

CO2 Emission

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1 INTRODUCTION

Motivation for this research dates back to 2006 where Hot Mud Flow (LUSI) started in

Porong district of East Java. It was natural gas explosion in an exploration well and a tragic event to be sure that brought crisis to its nearly surrounding area and Indonesia as well. LUSI disaster effect to transportation system was disruption of important highway for container transportation.

Some alternative routes post disaster as became source of congestion while the other is occasionally inundated by the mud. It affected container traffic from various hinterlands to Port of Tanjung Perak in Surabaya City, the second largest port in Indonesia. Under the LUSI disaster case, application of mitigation plan had conducted for hazard of losing total connection in container distribution in and out Port of Tanjung Perak, Surabaya. Mitigation steps and transport modeling were combined for creation of alternative networks that can be used soon after unexpected event occurred in the future.

This research examines the construction of transport model for decision support system in the mitigation process to minimize effect of unexpected event that follows a disaster. The objective is to examine available alternatives for container transportation network with minimum infrastructure investment. Contributions of this research are as follows: First, we develop integrated model that examine all availableCopy options for creating transport network alternatives including combining various modes, nodal points and routes to create options of solutions. We examine how marine based transportation can help to solve land based transportation problem.

Second, internalization of CO2 emission calculation is applied to represent external cost of transport production. In summary, focuses of study are on the following things:

1) Container cargo transportation with goal to minimize total transport costs

2) Mitigating unexpected event post disaster that may give adverse impact to transportation.

3) Applicable to case with complex choices in mode of transport, nodal points and routes

1.1 LITERATURE REVIEW

Emergency management covers all process from planning until establishment of physical body affected by disaster including transportation system by the implementation of emergency logistic. In disaster management, mitigation step is the phase that concerns the immediate reaction to the event and can involve the shutting down of transport systems, the evacuation of populations and the mobilization of first response resources. The goal is to control and attenuate the disruptions caused by the disaster 14).

Review on OR/MS research in disaster operation management showed that most of research was done in the term of preparation of disaster, but routing reaction-to-disaster related research retained little attention 2). Disaster-related research mostly discussed about distribution of emergency relief such as medicine and emergency relief 3),6),12),17) and less attention for business application such as cargo distribution. Previous research that closely focusing on examination of transportation cost in intermodal network flow model, but still for emergency relief was presented by Hagdani and Oh 6). It considered a shipment that can change from one mode to another at some given nodes, that earliest delivery times are given for some commodities and that arc capacity may be time-dependant.Copy Heuristic solution procedures were proposed for solving problems with three modes, three points of origins and two destinations (O/D).

Conceptually this study proposition is most similar to Berkourne et al.3) since they consider heterogeneous fleet, and various routes, capacity constraint and various nodal points. The different is that the latter research focused on emergency relief in disaster response phase in order to minimize total duration of all trip, while this study proposition is focused on cargo transportation with goal to minimize transport cost in disaster mitigation phase as basic pattern

Hangga, P. (2012): Transport Modeling and Mitigation Planning for Disrupted Transport Network, Final Thesis for Completion of International Master Course Program, Graduate School of Engineering, Department of Marine System Engineering, Kyushu University, Japan Hangga, P. (2012): Transport Modeling and Mitigation Planning for Disrupted Transport Network, Final Thesis for Completion of International Master Course Program, Graduate School of Engineering, Department of Marine System Engineering, Kyushu University, Japan for improvement in the next phase. Janic10) introduced important assumptions for modeling intermodal network was introduced and can be used for modeling transportation for disaster related management. Characteristic of transport mode and terminal must be taken seriously if one wants to combine it utilization to reach efficiency.

Achmadi and Hangga 1) examined the transport behavior after a disaster occurred particularly on Hot Mud Flow Disaster (LUSI) disaster and proposed short term route planning for container transportation that has been disrupted due to loss of network connection. Gurning 5) proposed a modal choice for encountering the same transportation problem of LUSI and comparing the reliability of different means of transport by its generated transport cost. This study will depend more on both the latter research result to generate a real model of disrupted transportation network prior to LUSI. Concern and understanding over global warming issues have rising since the introduction of Kyoto Protocol. In addition, reduction of CO2 emission is viewed as a significant contribution to minimize adverse impacts of climate change in sustainable cargo distribution policies. Research over carbon dioxide emission in container transportation and haulage showed relation that transport efficiency significantly affect the emission reduction 11),19).

1.2 OVERVIEW OF HOT MUD FLOW DISASTER IN EAST JAVA (LUSI) East Java Hot Mud Flow (LUSI)Copy as shown in Fig. 1, is a natural gas explosion in exploration well, owned by PT.Lapindo Brantas Inc (LBI), started in 2006 at Porong, District of Sidoarjo,

East Java Province, Indonesia. It was a tragic event to be sure and suddenly bringing crisis of social and economic not only to its nearly surrounding area, but also to Indonesia as well, since it was categorized as a major catastrophe when compared to similar cases in the world as can be seen in Table 1. Direct impact of LUSI has been felt by more than 17,000 citizens in local area which need resettlements for losing their houses. Citizen has lost more than 11 thousands homes

Hangga, P. (2012): Transport Modeling and Mitigation Planning for Disrupted Transport Network, Final Thesis for Completion of International Master Course Program, Graduate School of Engineering, Department of Marine System Engineering, Kyushu University, Japan Hangga, P. (2012): Transport Modeling and Mitigation Planning for Disrupted Transport Network, Final Thesis for Completion of International Master Course Program, Graduate School of Engineering, Department of Marine System Engineering, Kyushu University, Japan and two dozen businesses that have been buried in more than 6 sq.km under 20 meter deep which covers nine villages in Sidoarjo area with mud. LUSI disaster status has increased from regional disaster to national disaster due to its severances. Infrastructure also has been damaged extensively, including toll roads, power transmission systems, gas pipelines and national artery roads. Traffic to the south from Surabaya is still possible through Mojokerto or Mojosari Region, but the mainroad along Porong is occasionally inundated by the mud .This affects traffic from

Pasuruan to Surabaya vice versa.

Table 1 Comparison of LUSI disaster with similar phenomena in the world Koturdag Lokbatan Piparo LUSI (Indonesia, Characteristic (Azerbaijan, 1950 - (Azerbaijan, 2001) (Trinidad,2011) 2006) present) Volume (km3) 0.0003 0.00045 0.025 0.012 Duration 30 minutes 19.660 days 1 day 1460 days ** Area km2 0.098 0.3 2.5 6 Average Rate* 0.0144 0.000000025 0.025 0.00007 - 0.0015 Estimated thickness of the mud layer (Feb 2007) 10m - 18 m Estimated number of displaced people 11,000 - 50,000 * Cubic km/days ** As of February 2010

Copy LUSI impact area

Gempol Highway

Bird view Satelite Image

Source : Badan penanggulangan lumpur sidoarjo Fig. 1 LUSI disaster in Porong District, East Java, Indonesia

Direct financial cost as the value of cash that has been paid for the victims counted to be 516 million US$ and the total economic cost as the value of negative effects to the asset and people’s income counted to be 3.5 million US$ 3). There are uncertainties in spreading rate of volcano and long term effect to environment, urban areas and infrastructure and many forecasted that it will last for a very long time, averagely within 30 years 4),13).

1.3 OVERVIEW OF DISRUPTED TRANSPORT NETWORK POST LUSI

1.3.1 Role of Port of Tanjung Perak in Container Transportation

Port of Tanjung Perak (PTP) in Surabaya City, Indonesia, has been served as important gateway for both domestic and international cargo distributions. Surabaya Container Terminal

(TPS) and Berlian Jasa Terminal Indonesia (BJTI) are two sophisticated terminals to handle domestic and international containers with more than 1.5 million TEU handling capacity each year. Approximately 4,600 TEU of both international and domestic cargoes flowing daily in and out of Port of Tanjung Perak with a composition of 50% for domestic orientation, 31% for imports, and 19% for exports 1),5). In the future development of containerization in Indonesia,

Container Terminals in Port of Tanjung Perak expected to expand its role as Hub Port for East Indonesia cargo distribution and openingCopy direct route to Major Port in Asia and the World as shown in Fig.2. East Java Province has been one of Indonesia’s highest economically growth, with Surabaya city as its center which supported by other nearest province such as Bali and Nusa

Tenggara (NTB). Connectivity between hinterland and PTP is highly important to keep balance of transport network, and inefficiency occurred in the network after LUSI arises opportunity to develop other port as an alternative hub to Port of Tanjung Perak (PTP).

1.3.2 Current Situation of Transportation in Disrupted Network

After Hot Mud Flow (LUSI) disaster happen in 2006, the main hinterland transportation network, namely Gempol Highway, which connects Surabaya City with Eastern Region in East

Java and South Eastern Island of Indonesia, mainly Bali Island, and West Nusa Tenggara (NTB), has been disrupted and covered by mud. Transportation times have increased for cargoes bound for Port Tanjung Perak and road congestion has increased trucking times to Surabaya from four hours to around ten hours 1). Delay and congestion occurred in container haulage transportation post LUSI shows the decrease of reliability of transportation. Early Short-term mitigation by redirecting container traffic flow to Mojosari alternative road is conducted by local authority, but fail to reduce congestion. Learning from historical experience, transportation must not be disrupted again, thus more alternative must be presented i.e worst case scenario; where current network cannot be utilized anymore. Copy

Fig. 2 Port of Tanjung Perak as Main Gate for Cargo Distribution

Transport Freight 360 (US$) 330 300 270 240 210 180 150 120 90 60 30 Distance Range (Km) 0

20 ft size 40 ft Size 20 ft Size (After LUSI) 40 ft Size (After LUSI) Fig. 3 Transport freight for container cargo prior to LUSI

This hypothesis based on the facts: (1) uncertainty of LUSI’s spreading area and high possibility of sudden phenomenon occur in future and (2) no confident judgment published yet, stating that the surrounding area will be safe enough for certain time. Preceded research 1) examined the bottleneck of container transport network prior to LUSI. Some intersection along the alternative network potentially causes bottleneck which leads to traffic jam since it was mainly designed not for heavy modeCopy transportation. Unit delay of haulage transport is 0.05 hours/km compared to normal condition with additional external cost of 6.7 US$/hours of delay.

Fig. 3 shows the door-to-door transport freight dependence for several distance range. Increase in transport freight on each distance range can be seen as effect of LUSI disaster to transport freight.

The phenomena must be considered in wider context which reflected the decreasing of quality and reliability of transportation. Fig. 5 shows illustrated example of examined bottleneck post

LUSI disaster.

Fig. 4 Transportation network condition after LUSI disaster Source : Various sources from internet

To Porong SURABAYA SEPANJANG Port of Tanjung Perak To Mojosari Vavg = 50 MOJOKERTO km/hrs (PTP)

To Malang KRIAN Police WONOAYU SIDOARJO Station

TULANGAN TANGGULANGIN LUSI Bottleneck Area MOJOSARI

Vavg =10 PORONG km/hrsCopy Vavg = 40 km/hrs NGORO KEJAPANAN INDUSTRIAL AREA TRAWAS GEMPOL JAPANAN PRIGEN INTERSECTION PANDAAN DISRUPTED HIGHWAY N RE-ROUTE FOR PRIVATE CAR

Vavg = 60 km/hrs Vavg = 40 RE-ROUTE FOR CONTAINER km/hrs TRANSPORT

Fig. 5 Examined bottleneck in container transportation post LUSI

2 METHODOLOGY FOR DISASTER MITIGATION PLANNING

2.1 PROBLEM OF LAND TRANSPORT-CENTRIC

For container transportation, it cannot be denied that land transportation is the first and mostly used mode of transport in the world. The use of trucking in container transport and its distribution present in every line of transport supply chain, from hinterland transport, depot-to- depot, inside terminal transport and end delivery. Uses of container trucking have many benefits.

It gives cargo owner/freight forwarder the freedom of mobility that allows quick and convenient movement of the cargo, wherever and whenever they wishes. Compared to container ship or freight train, container truck can be owned by personal or cargo owner thus increase personal utility. Land transport infrastructure also the most basic infrastructure and almost impossible to transport without the presence of it thus gives utilization of container trucking the most flexible and connectible modes.

However, the whole systems is paying high price for those benefit. The cost of owning, maintaining, and repairing for all of the operable container trucking in the system may larger than the same cost for container ship or freight train with the same transport capacity.

Additionally, total movement of all container trucks may produce larger total distance than the other two substitute modes, thus Copy generates higher level of waste and emission. The more container trucks used in the system without substitute or complementary modes, the more it damages will be inflicted on society and economy. There are also higher risks for accident although it will not be quantified in this research. Moreover, the growth of land modes is not proportional with the growth of its supporting infrastructure, leaving bottleneck and congestion in some parts of the network.

In this study, the author emphasizes the importance of intermodal transportation in container cargo distribution and assessing the characteristic of each mode of transport as shown in Table 2.

Through proportional utilization of other modes of transport in the network, the total cost of system can be minimized into beneficial level of transport performance. The concept of modal shift is employed with the benefit illustrated in Fig. 6.

Maximize personal Minimize utility environmental cost

Minimize total cost of transportation system

Minimize total generated distance

Maximize movement Maximize total flexibility social benefit

Fig. 6 Benefit of modal shift in intermodal transportation system

Table 2 Mode of transport characteristic for container transportation Mode No Variable Container Trailer Truck Freight Train Inter Island Container Ship Transport 1 Capacity 1 70-100 100-500 (TEU) 60-70 km/hr Copy70-80 km/hr 7-15 kn 2 Service Speed Depends on hull structure and M/E Depends on land traffic Depends on track size Relatively do not need special Need special infrastructure Need special infrastructure infrastructure except adequate (train track) and supporting (container terminal) as well as 3 Needed Infrastructure road network dry port and land transport Container yard, CFS, Depot network and land transport network Service Local, relatively door-to-door Local, relatively point-to-point Regional, relatively point-to-point 4 Characteristic service service service

Relatively low, depends on traffic Relatively high, have designated Relatively high, have stric operating 5 Operational safety condition track and fixed safety procedure regulation and fixed safety procedure

Service 6 Daily Daily Daily, Per 2 Day, or Weekly Frequency 7 Time Value Relatively High Relatively High Medium to Low 10

2.2 MITIGATION STEPS FOR SOLVING DISRUPTION IN CONTAINER

TRANSPORTATION: APPLIED IN LUSI DISASTER CASE

By knowing the problem in the transportation network as well as the advantages and disadvantages of transport modes employed in container transportation, we can be prepare the mitigation plan in case of any disaster happen disrupting the network. However, there are modal and technological disparities between countries that will make mitigation treatment differ from one another. To generalize the situation, mitigation steps in this paper is designed to provide solution that optimizes available infrastructures and alternative networks with without considering additional investment in new infrastructures. Mitigation method in this study is focused on unexpected event post LUSI disaster that may have adverse impact to container cargo distributions. Alternatives for solution were clearly depicted in mitigation steps that integrated transport modeling. Steps in providing solution to mitigate worst case scenario of LUSI were as follows:

1) Cargo Forecasting

Basic model for cargo forecasting is time series forecast with seasonal demand. Earlier result

was conducted in earlier research and detail of forecast can be seen in the publication1) . That result became base pattern of forecastCopy in this study with detail cargo composition in Table 3. Forecasted containerized cargos that flow around the network were 4600 TEUS/day in total.

2) Generation of consolidation point

Consolidation point in concept is a place located within the distribution network that

consolidate some of the cargo that passed by before transported to next destination point

using more efficient means of transport. The objective of consolidation point is to minimize

total transport cost and optimize the utilization of transport mode. Sea port and dry port are

means of consolidation point as it provide the storage area and handling equipment.

Summary of measured data for all prospective consolidation point is presented in Appendix

A. The consolidation point concept, or some would say feeder nodal point, helps to

distribute cargo in large volume within one shipment with land transport act as connection to

consolidation point as the outlet with it served hinterlands. It also minimizes the individual

risk of transporting the cargoes individually by transferring it into larger scale means of

transport. Important considerations for the selection of consolidation point are Infrastructure

scale, geographic scale and market intensity scale. Fig. 7 shows the cluster concept for

consolidation point, mainly sea ports, based on the characteristics mentioned and the cluster

result is shown in Table 4. Fig. 8 Illustration of consolidation point along disrupted network

and shows selected consolidation points that were used, notably sea ports and dry port.

Table 3 Forecasted containerized cargo that flow around examined network

Domestic Cargoes International Cargoes Forecasted No. Region Cargoes AB C D E (TEU/day) Handcraft, Handcraft, Art Handcraft, Art Electronics, 1 Bali Raw Materials 998 Art Craft Craft Craft G.C. Agriculture, Natural Electronics, 2 NTB Agriculture Raw Materials 925 Natural Resources G.C. Agriculture,Resources Electronics, 3 Banyuwangi Agriculture Raw Materials Fishery products 262 CopyG.C. G.C. Electronics, Electronics, Wooden Electronics, 4 Pasuruan Plywood 766 Cigarrate Cigarrate Furniture, G.C. Agriculture, Agriculture, Cigarrete Electronics, 5 Probolinggo Raw Materials Fishery products 216 Fishery Fishery G.C. ProductsGeneral Products Wooden Electronics, 6 Malang G.C. Raw Materials 390 Cargo Furniture G.C. General Electronics, Semi processed Electronics, 7 Surabaya Raw Materials 1044 Cargo G.C. materials G.C. Notes : A : Bound for Surabaya and Western East Java D : International Exports B : Shipped to other domestic port E : International Imports C : Shipped from other domestic port

NODES (SUPPLY SIDES) TYPE OF PORT PORT NAME Investment Scale Service & Facility Scale LOCAL Benoa (BALI) PORT Bima (NTB) VERY SMALLER MODERATE BIGGER VERY BIG SMALL Celukan Bawang (BALI) VERY SMALL LOCAL Ende (NTT)

SMALLER REGIONAL Probolinggo (EAST JAVA) Kupang (NTT) MODERATE NATIONAL Lembar (NTB) BIGGER INTL. SHIPPING Pasuruan (EAST JAVA) VERY BIG INTL. HUB REGIONAL Tg. Wangi (EAST JAVA) VERY SMALL LOCAL (Abbreviated as PTW) SMALLER REGIONAL Geographic Scale

Market Intensity Degree Intensity Market GOVERNMENT MODERATE NATIONAL LINKS (DEMAND SIDE) (DEMAND LINKS NATIONAL Tg. Perak (EAST JAVA) BIGGER INTL. SHIPPERS (Abbreviated as PTP) VERY BIG INTL. HUB Fig. 7 Cluster concept of consolidation point Table 4 Cluster result of consolidation point (Sea port) for LUSI mitigation

Sea Port Freight train Dry Port Surabaya/ Delivery Point Railway

Dry port CopyPasuruan

Fig. 8 Illustration of consolidation point along disrupted network

JAVA SEA JAVA SEA

N N A TUBAN B TUBAN C MADURA ISLAND C MADURA ISLAND Kalianget ( Sumenep ) Kalianget ( Sumenep ) E GRESIK E GRESIK N N Port of Tanjung Perak Port of Tanjung Perak T Babat Branta (Pamekasan) T Babat Branta (Pamekasan) LAMONGAN LAMONGAN R BOJONEGORO R BOJONEGORO A SURABAYA A SURABAYA L SIDOARJO L SIDOARJO

MADURA STRAIT MADURA STRAIT J NGAWI J NGAWI MOJOKERTO Mojosari MOJOKERTO Mojosari A Gempol Gempol Japanan SITUBONDO A Japanan SITUBONDO Bangil Bangil V Caruban V Caruban NGANJUK JOMBANG NGANJUK JOMBANG A Pandaan PASURUAN A Pandaan PASURUAN Maospati Kertosono Maospati Kertosono Purwosari MADIUN PROBOLINGGO MADIUN Purwosari PROBOLINGGO

MAGETAN MAGETAN BONDOWOSO BONDOWOSO KEDIRI KEDIRI Lawang Lawang Ketapang Ketapang

MALANG Meneng Meneng JEMBER MALANG JEMBER PONOROGO PONOROGO BLITAR BLITAR LUMAJANG TRENGGALEK BANYUWANGI TRENGGALEK LUMAJANG BANYUWANGI Gilimanuk Rambipuji Rambipuji Gilimanuk TULUNGAGUNG TULUNGAGUNG BALI BALI ISLAND ISLAND Puger Puger PACITAN PACITAN Benoa Benoa INDONESIAN OCEAN INDONESIAN OCEAN

JAVA SEA JAVA SEA

N N C TUBAN D TUBAN C MADURA ISLAND C MADURA ISLAND Kalianget ( Sumenep ) Kalianget ( Sumenep ) E GRESIK E GRESIK N N Port of Tanjung Perak Port of Tanjung Perak T Babat Branta (Pamekasan) T Babat Branta (Pamekasan) LAMONGAN LAMONGAN R BOJONEGORO R BOJONEGORO A SURABAYA A SURABAYA L SIDOARJO L SIDOARJO

MADURA STRAIT MADURA STRAIT J NGAWI J NGAWI MOJOKERTO Mojosari MOJOKERTO Mojosari Gempol Gempol A Japanan SITUBONDO A Japanan SITUBONDO Bangil Bangil V Caruban V Caruban NGANJUK JOMBANG NGANJUK JOMBANG A Pandaan PASURUAN A Pandaan PASURUAN Maospati Kertosono Maospati Kertosono

MADIUN Purwosari PROBOLINGGO MADIUN Purwosari PROBOLINGGO

MAGETAN MAGETAN BONDOWOSO BONDOWOSO KEDIRI KEDIRI Lawang Lawang Ketapang Ketapang Meneng Meneng MALANG JEMBER MALANG JEMBER PONOROGO PONOROGO BLITAR BLITAR TRENGGALEK LUMAJANG BANYUWANGI TRENGGALEK LUMAJANG BANYUWANGI Rambipuji Gilimanuk Rambipuji Gilimanuk TULUNGAGUNG TULUNGAGUNG BALI BALI ISLAND ISLAND Puger Puger PACITAN PACITAN Benoa Benoa INDONESIAN OCEAN INDONESIAN OCEAN

A : Sea routes – coastal shipping B : Land routes – from East Java Province C : Sea routes – inter island shipping D : Railway routes - from East Java Province Fig. 9 Example of generated routes in intermodal transport for LUSI disaster mitigation

3) Routes generation

Alternatives routes were created in this process through intermodal transportation by

utilization of nodal points and consolidation point that connect hinterlands to delivery point.

For mitigating LUSI disaster, total of 24 new routes are created as exemplified in Fig. 9.

Detail of all generated routes can be seen in Appendix B 4) Solution generation Copy After the entire predecessor steps are conducted, the final step is to create solution for

mitigating the disrupted network is through transport modeling. In the application of this

study, the disrupted transport system by LUSI disaster is modeled, and movement of cargo

was simulated computationally with mathematical model explained in next section. In

addition, projected emission of network solution is calculated to emphasize environmental

friendly approach of the solution.

3 MATHEMATICAL MODEL OF PHYSICAL DISTRIBUTION

Fig. 10 illustrated the concept and framework of disaster mitigation in cargo distribution sector by intermodal transportation. This study emphasize the utilization of intermodal transport consist of combination of transport modes. Important point that this study try to emphasize is the utilization of marine systems i.e: ship, barge, marine terminals to provide support for mitigating a land based disaster.

In theory, mode consideration must be equal to one another in order to produce optimum solution, but in practical land based means of transport usually get more handicap than freight or marine transport. Common reasons is that both freight and marine transport require high investment of infrastructure with slow return of investment which is true, but in term of disaster mitigation, where decision must be made in a short period of time with minimum investment of supporting infrastructure.

DISASTER

International CopyDISASTER Destination

Domestic Destination

Domestic Origin Current land route Current rail route Alternative route

Fig. 10 Concept of intermodal connection in disaster mitigation of cargo transportation

Decision making process will focus mainly on developing suprastructure which includes the planning, network design and network optimization. In other word, optimum solution must be generated with only the available resources, infrastructure, variation of mode and transport route to generate solution for alternative network.

3.1 MODELING ASSUMPTION

Table 5 summarizes notations that are used to model transportation network. Let i1 be the given number of hinterland as the source of containerized cargo and i2 represent the number of delivery point for containerized cargo. Total number of containerized cargo, denoted as xi distributes within the network through transportation routes Rj, from various hinterland denoted as si2 to designated delivery points denoted as di1. Amount of cargoes in hinterlands are assumed to be sufficient enough to cover the supply limitations in the delivery point. With respect to intermodal transportation, there are various modes of transport that can be utilized, denoted as i3.

Deployment of each mode is assumed to be sufficient enough to cover the demand.

Utilization of various nodal points and modal split produce different travel and handling time, thus creates trip limitation for each type of mode. Production of time in transportation is another important variable in decision making. Utilization of various nodal points and modal split produced different travel and Copy handling time, thus creates trip limitation for each type of mode of transport. In this paper, both are taken into account to get number of trip limitation per operation day and eventually lead to maximum deployment during mode operation, denoted as i4. Determining travel time post disaster is important since updated information on the state of the transportation infrastructure is needed in to obtain reliable estimation 18). Updated information of actual handling and transport time on the state of the transportation infrastructure is examined by site surveys in several nodal points during fiscal year 2007 – 2010.

Table 5 Notation of assumption used in modeling Notation Index Explanation

R j Intermodal transportation routes Quantity of cargo to be distributed for each transportation routes using x i1,i2,i3,i4,i5,i6 specified modes

c i1,i2,i3 Internal costs of transport + extra surcharges because of congestion Emission cost, counted paralelly with internal cost based on distance c e i1,i2,i3 production

Q i3,i4,i6 Total carrying capacity limitation for all utilized mode of transport

s i1 Quantity of containerized cargoes need to be distributed from hinterlands

d i2 Quantity of containerized cargoes that can be captured by delivery point

i 1 i 1 =1.... n 1 Number of hinterland as the source of containerized cargo

i 2 i 2 =1.... n 2 Number of delivery point for containerized cargo

i 3 i 3 =1.... n 3 Modes of transport choice for container transportation Maximum deployment for each mode of transport (Based on travel time and i i =1.... n 4 4 4 handling time)

i 5 i 5 =1.... n 5 Route capacity constraint for accepting cargo i 6 i 6 =1.... n 6 Carrying capacity constraint for each mode of transport

Table 6 Modal capacity assumption for intermodal means of transport

Max Deployment {unit

/ day} *) Emission Unit Service Fuel Cons *) *) Mode Carrying Capacity Very Cost Emission Speed Smaller Bigger l/km Small USD/TEU/km t/km Port Port Port Marine 3400 tons 300 TEU 7 kn 1 2 4 1.10 0.28 0.000012 Railway 1190 tons 70 TEU 70 km/hr 1 2 2 1.30 4.18 0.000048 Land 17 tons 1 TEU 80 km/hr 200 600 1200 2.54 1.35 0.000094 *) standard from INFRAS/IWW (8), convertedCopy to USD

Each route segment has limitation for amount of cargo passing through, in which depends on the infrastructure carrying capacity, denoted as i5. In addition, there are restrictions for capacity i6 for each means of transport, that represent economic of scale. Table 6 shows modal capacity assumption for the applied case followed by set of assumption for modeling intermodal network.

Since the disrupted network locates between supplies chains in a huge system, prospective cargo

3.2 INTERNAL AND EXTERNAL COST

The internal cost components are embraced by the cost structure shown in Table 7. Costs data for land and marine transport were collected from actual Indonesian container freight market during fiscal year 2007 - 2010. Since there is several size of container ship that can be operated in Indonesian waterways, the author would like to focus more on small size container ship which is commonly employed in interisland shipping in Indonesia, ranging from 200 – 300

TEU capacity. Nevertheless, railway transport costs data is taken from publication by INDII 8).

The capital cost represents fixed cost of means of transport, while operating cost, and overhead cost is a variable cost that is linear with the commission days of the vehicle. In this study commission days for all vehicle is assumed to be fixed for 230 day per year. Fuel cost is assumed to be using a fixed price within a year without fluctuation. Another variable cost is cargo cost, which depends on how much cargo that will be transported, port charges and cargo handling cost. In this study, the entire vehicles are assumed managed to transport until its maximum capacity without any broken space. External impact of transport thatCopy reflects fuel consumption, mileage, and environment impact is represents by CO2 emission. Various estimates can be found in the context of individual studies especially for road transport but no reference appears for investigation of different transport modes emission. Therefore, standards for generated emission from INFRAS/IWW 9) as shown in Table 8 are employed in calculation due to lack of similar standard in Indonesia. It includes the measurement of inland waterways and trucking for freight transport that reflect external costs of transport.

Table 7 Internal and external cost structure for construction of total transport cost Internal Cost Domain External Cost Capital Cost Operating Cost Cargo Cost Overhead Cost Mode Investment Fuel Cost Lo/Lo Repair Emission Land * Infrastructure Labor Cost Document Cost Maintenance Congestion Insurance General Affair Cost Mode Investment Fuel Cost Lo/Lo Repair Emission Infrastructure Labor Cost Storage Cost Maintenance Congestion Rail ** Insurance General Affair Cost Handling Cost Document Cost Mode Investment Fuel Cost Transhipment Cost Repair Emission Infrastructure Manning Cost Handling Cost Maintenance Congestion Insurance General Affair Cost Port Charges Marine* Ship Management Storage Cost Haulage Cost Document Cost

* Indonesian market freight 2007-2010, survey on 37 companies, elaborated, ** Indonesia Infrastructure Initiative (INDII): The Market for Railways in Indonesia (http://www.indii.co.id/) *** INFRAS/IWW: External Costs of Transport -Update Study, Final Report

Table 8 Basic assumption for calculation of transport emission CO2 Emission Cost Assumption Cargo 1000 Ton Distance 700 km (Rotterdam to Basel) Generation of CO2 Truck 4.7 Ton Inland Waterway 2.4 Ton Rail 0.6 Ton

Energy Efficiency Assumption Cargo 100 Ton Distance 700 km Fuel Consumption Truck 1779 diesel litre InlandCopy Waterway 770 diesel litre Rail 911 diesel litre

External Cost of Transport Externalities Included Accident, Air Pollution, Noise, Landscape Impact Externalities Excluded Congestion Fuel Consumption Road 0.0878 Euro/Ton/km Inland Waterway 0.0179 Euro/Ton/km Rail 0.2713 Euro/Ton/km Aviation 0.0255 Euro/Ton/km Source: INFRAS and IWW, 2004, External Costs of Transport - Update Study, Final Report, Study on behalf of International Union of Railways (UIC), Zurich (8)

3.3 MATHEMATICAL MODEL

It is possible to minimize total cost by distributing amount of prospective cargoes to several potential networks through modeling, based on the principal of balanced transportation that mathematically express by the following objective function:

e ZMinimize  x   cc   i1 i, 2 i, 3 i, 4 i, 5 i, 6 i1 i, 2 i, 3 i1 i, 2 i, 3 (1) (i1 i, 2 i, 3 i, 4 i, 5 i, 6 )Rj

Under restrain conditions:

x  d (2)  i,i,i,i,i 65432 i1 =1…..n1 65432 R,i,i,i,i,i j

x  s i2 =1…..n2  i,i,i,i,i 65431 (3)

65431 R,i,i,i,i,i j

i =1….. n 3 3 Qx (4)  i,i,ii,i,i 643521 i =1….. n 4 4 (i 521 R)i,i, j

i =1….. n 6 6 i =1….. n  sd Copy 1 1 (5)   j RiRi j i 2 =1….. n2 2 1

( 5)

 0x  (i R)i,i,i,i,i, j 654321

Here, objective function (1) minimize the total cost (internal and external cost) of transportation. Internal cost of transportation that are generated by the simulation denoted as ci.

e The external cost from emission, denoted as c i are calculated in respect to distance that are generated by internal cost calculation. Amount of projected emission is calculated by multiplication of unit cost of CO2 emissions to total distances that are generated by the same internal cost calculation. Constraint Eq. (2) ensures that each delivery point receives the requested quantity of cargo and in the same manner, constraint Eq. (3) ensures that each hinterland deliver cargo not to exceed its capacity. Constraint Eq.(4) imposes the carrying capacity constraint of each mode of transport. Constraint Eq. (5) ensures the quantity of cargo available in the hinterland sufficient enough to cover the demand in the delivery point.

Constraint Eq. (6) represent the non-negativity assumption of the transported cargo.

Calculation by matrix is employed and method to search optimum condition in matrix

(7),(16) operation has been introduced and used in this paper . Let xij defined a group of combinatorial element to satisfy constrained condition and denoted as initial matrix X0 .

a1  11  xx 1m  (6) X0       Copy   xx   n1 nm an

b1 bm

+1 or -1 is added to the combinatorial elements of two rows and two columns within initial matrix X of Eq. (6 ) for balance keeper of restrain, and then X0 , is given after this operation. 0 ij kl Calculation process shown in following Eq. (7).

 x11  x1m  a1   x 1  x 1 a  ik il  k 0 (7) X ij,kl     é x  x ù  11 x 1  x 1m1 a ê  x +1jk x -1 jl ú  l ê ik il ú x  x  an X0 = ê  m1 ú mn  ij,kl ê ú b1 bk bl bm ê x jk -1 x jl +1 ú ê ú ë xn1 xnm û = X0 + DX0 ij ,kl

Remarks :

(ak, bk), (ak, bl), (al, bk), (al, bl), = random place in the matrix

The matrix X0 , shall satisfy x ≥ 0, including the case ΔX0 , = 0 within the total ij kl ij ij kl combinations of 2 C C +1 at the greatest. Optimum search is taken among these combinations. n 2 m 2 When the matrix that solve objective function is found, X1 is denoted as the new initial matrix.

1 0 0 X = X + DX (8)

s s 0 t Copy ( 9) X = X + å DX t = 0

The convergence value is obtained by repeating the operation of Eq. (7) and Eq. (8). The final solution after those repetition will be gained by Eq. (9). In addition, when dealing with large number of combinations in the process of equation (7), genetic algorithm can be used to reduce computation time, but in the applied case of this study linear programming is enough to

O D

Land transport segment Direct route from O / to D Marine transport segment Modal Split

Rail transport segment

Fig. 11 Illustrated model of route computation by proposed mathematical model

Copy

4 APPLICATION OF MITIGATION PLANNING AND TRANSPORT

MODELING IN LUSI DISASTER CASE

4.1 SETTING OF CONDITION

Model and assumptions are applied to disrupted transportation network problem post LUSI disaster that had briefly examined in previous research 7). Origin and destination are set and known, notably 7 hinterlands and 1 delivery point, namely Port of Tanjung Perak, Surabaya

(abbreviated as PTP). The latter is the second largest container terminal in Indonesia that has handled 1.5 million TEU averagely each year and an important gateway for distribution of both domestic and international cargo. After LUSI disaster, important node that connects hinterlands to PTP, namely Gempol Highway is totally disrupted. Distribution time to PTP from nearby hinterland within 330 Km range has increased from 4 to 10 hours as congestion appears (1).

Updated condition of transport cost and infrastructure are examined before calculation.

4.2 APPLIED MITIGATION STEPS

Fig. 12 shows the exemplified the framework of mitigation steps for LUSI disaster as explained in chapter 2 and elaborated as follows: First, covering area of cargo sources is set not to exceed 900 Km to the east of EastCopy Java Province. Prospective container cargoes from all hinterlands consisted of domestic and international cargoes, including both inbound and outbound cargoes, to be 4,600 TEUS/days with composition of 50% for domestic orientation,

31% for imports, and 19% for exports. Kinds of containerized cargoes are wooden furniture, packed cigarettes, semi processed material, and general cargo.

Forecasting of Disrupted Cargo and Collection of Updated Cost -Primary survey conducted on 6 shipping companies, Transport cost data collection 30 freight forwarders, 1 railway company -Actual cost data elaborated from Indonesian shipping market Amount of cargo data collection during 2007 – 2010 -Disrupted cargo was forecasted by time series method with data collection from 2003-2010 (Achmadiand Hangga, 2007)

Transportation Routes Generation

Consolidation Point Generation -Set of Consolidation points : 11 Sea Ports, 1 Dry Port,

-Infrastructure data to be evaluated are : Infrastructures Evaluation -Port : Berth, LWS, Storage, CY, Handling Equipment -Rail : Track (single/double) , Capacity, Handling equipment

-Develop mathematical model that represent network condition Transport Modeling -Employs developed optimum value searching algorithm proposed by (Shinoda and Fukuchi, 2005)

-Modeling solution is compared with network condition Analysis of Modeling Solution before disaster (BD), and network condition after disaster (AD)

Fig. 12 Mitigation framework for transport disruption in LUSI disaster case

Actual transport cost data are collected during fiscal year 2007-2010 from following sources;

6 container shipping companies, 30 freight forwarders and 1 railway company. Additional cost information is gained, notably extra fuel consumption because of congestion. Second, all options of routes andCopy nodal points that link the hinterlands to delivery point are employed to generate new alternative transport system. Nodal points that connect intermodal transportation are set as prospective consolidation points, notably 4 sea ports and 1 dry port along the coast of East Java and 7 sea ports located in other islands. 24 new routes are created that link various nodal points in all domains except air transportation.

Third, based on the result of two previous steps, transport modeling is conducted which employed the mathematical model and algorithm presented in chapter 3. Calculation to reach

Intel Core i3 running at 1.8 Ghz with 2 Gig RAM. Computational simulation using the proposed algorithm was conducted by using C programming language.

To measure the reliability of the proposed algorithm, separate computation for the same case is conducted by employing common algorithm and method for solving transportation problem i.e. Simplex for linear programming. Pre-test was conducted case using randomly generated transport problem and compared the result between both algorithm. For this purposes, application of simplex method was conducted in Microsoft Excel program where Simplex algorithm for linear programming problem solving is included in its built-in Solver application.

Example of case study to measure the algorithm ability to deliver optimum solution is presented in Appendix C.

Copy

S5 S6

S1 S2 S3 S4 S S S S S

h1 h2 h3 h4 h5 h6 h7

L1 L2 L3 L4 L5 L6 O L O L O O L L D NTB BAL BAN PAS SUB h8 h9

R1 R R

D Delivery Point / Destination L Land transport segment h Modal Split by handling O Hinterland / Origin S Marine transport segment Normal cargo Consolidation point (Port/Dry Port) R Rail transport segment Consolidated cargos

Total Internal Total Route Example Cargo Origin Cost (x * c), Distance Remarks (Summation of Segments) USD/TEU (km)

L1+h1+S5+S6+h7+L6 147 360 Cargo consolidated in BAN

NTB L1+h1+S5+h4+L7 337 702 Cargo not consolidated

L1+h1+S5+h4+L3+h8+R1+h9+L9 332 572 Cargo consolidated in PAS

L2+h2+S2+h4+L7 258 536 Cargo not consolidated

BAL L2+h2+S2+h4+L3+h8+R1+h9+L9 311 616 Cargo consolidated in PAS

L2+h2+S2+S6+h7+L6 142 367 Cargo consolidated in BAN

L7 189 322 Cargo not consolidated BAN L3+h5+S4+h7+L6 Copy147 286 Cargo not consolidated h3+S6+h7+L6 126 351 Cargo consolidated in BAN

L8 53 92 Cargo not consolidated

PAS h8+R1+h9+L9 163 115 Cargo consolidated in PAS

h5+S4+h7+L6 116 76 Cargo not consolidated

Note : BAL = Bali Province, BAN = Banyuwangi region, PAS = Pasuruan region. NTB = West Nusa Tenggara Islands, SUB = Surabaya / PTP

Fig. 13 Simplified scheme of intermodal network and example of routes calculation

Table 9 Example of cargo movement in modeling solution

Destination INTERNATIONAL CARGO DOMESTIC CARGO SURABAYA SURABAYA No \ Banyuwangi Direct to Surabaya Banyuwangi Direct to Surabaya Bali Demand Origin L S L S S-L S-L S-S L S S-L-S-L 1 1 Bali - 630 - - 630 368 368 - - - 998 2 2 NTB - 488 - 488 - - 437 - - 437 925 3 3 Banyuwangi 101 - 101 - - - - 161 161 - 262 4 4 Pasuruan - - 329 - - - - 437 - - 766 5 5 Probolinggo 101 - 101 - - - - 115 - - 216 6 6 Malang - - 160 - - - - 230 - - 390 7 7 Surabaya - - 491 - - - - 552 - - 1,043 202 1,118 980 - - - 805 1,334 161 - Supply 2300 2300 4,600 Notes : L = Land transport = Difference in way of transport ;current situation and modeling S = Sea transport = Cargo consolidation point S-L = Combination of sea & land transport S-S = Combination of 2 port in sea transport

N TUBAN

C MADURA ISLAND E Kalianget ( Sumenep ) N T Branta (Pamekasan) R SURABAYA D A TURI TRAIN STATION L Dry Port O J MADURA STRAIT Mojosari OTHER A MOJOKERTO Japanan SITUBONDO EASTERN V PASURUAN ISLAND A Dry Port Pandaan O Purwosari O PROBOLINGGO BONDOWOSO Land Transport Lawang Marine Transport JEMBER O MALANG O O PTW Port of Tanjung Wangi Rambipuji BANYUWANGI Gilimanuk Container BALI PTP Depot Port of Tanjung Perak Copy ISLAND O Benoa

D Delivery Point / Destination Distributed via Container Trailer O Hinterland / Origin Distributed via Smaller Container Ship

Consolidate Cargo distributed via 300 TEU Container Ship Consolidation point (Port/Dry Port)

Fig. 14 Illustration of network solution from computational simulation

4.3 MODELING RESULT

Examples of generated intermodal network are shown in tabulated in Table 9. Fig. 14 illustrates network solution from computational modeling that simulates cargo movement from hinterlands to delivery point. The comparison of algorithm for all 20 cases resulted in same results for both algorithm. Slightly different in performance is in the solving time. Simplex in

Excel Solver requires averagely 3 minutes while our proposed algorithm in C programming language requires averagely 11 minutes. Interesting fact that is, when we use Excel Solver, optimal solution in less than 4 pre-test case did not found optimal solution in the first running of the program. Optimal solution only founded by the second or third attempt in running the solver application. This anomaly will not be discussed in this article, as our objective is to proved that the result of both algorithm are the same.

4.4 ANALYSIS OF SOLUTION

Transport modeling and simulation of cargo movement are conducted to search optimum solution of the problem with example result shown in Table 9 and Fig. 14. Each cargo from every hinterland is provided with choice to utilize available mode for node-to-node travel until it reaches the final delivery point. Although 3 choices of modes are employed, not all choices are available in every traveled route segment.Copy In the simulation, mode utilization was strongly dominated by land and marine more because rail mode was not available in almost route segment. Achieved simulation result is compared with network condition before LUSI disaster occurred (abbreviated as BD) and after LUSI disaster occurred (abbreviated as AD) to show the effectiveness of the solution in mitigating transport disruption for LUSI case. Table 4 shows comparison of transport production that include internal cost, external cost and projected

AD and BD condition are explained as follow:

1) 46% of cargoes accounted for 2,125 TEU/day from total of 4,600 TEU/day, that originally

went directly to PTP by combination of container trucking and ferry transport, have

redirected and consolidated in Port of Tanjung Wangi (abbreviated as PTW) in BAN region.

Source of these cargoes particularly from Bali Island and Other Eastern Island. Consolidated

cargo then transported to PTP by a 300 TEU containership. Cargo handling and haulage in

marine terminal are counted when cargo is consolidated in a terminal.

2) As a consequence to 1), utilization ratio of land and marine transport became 40-60 ratios,

changing from 90-10 ratios in both BD and AD condition. 40% of cargo shift from land to

marine transport. By modal shift, 39% reduction of total internal cost is gained compared to

total production during AD condition, which accounted to USD 140,179/day.

Table 10 Average transport cost comparison between simulated conditions

Condition Information Measurement Base Unit Mitigation AD BD

International Cargo US$/TEU 79 161 148 Avg Transport. Cost Domestic CargoCopy US$/TEU 96 139 157 Avg Unit Transport. International Cargo US$/TEU/Km 0.00020 0.00024 0.00023 Cost Domestic Cargo US$/TEU/Km 0.00032 0.00023 0.00025

Land Km 272,789 807,721 725,427 Total Distance Sea Km 550,472 467,636 346,332

Note : Mitigation = Achieved network condition after modeling mitigation plan for worst case scenario BD = Modeled network condition before disaster occurred AD = Modeled network condition after disaster occurred

Table 11 Total cost of transport for modeling solution, condition before LUSI and post LUSI

Generated Emission Total Internal Emission cost (USD) Total External Total Total Network (ton) Cost (x * c) Cost ( x * ce ) Transport Emission Condition (USD) LRS (USD) Cost (Z) LRS (ton)

Modeling 220,074 182,732 - 66,307 249,039 469,113 12.7 - 14.3 27.0 Result

Before 320,063 326,261 N/A 17,626 343,887 663,950 36.4 - 8.0 44.5 Disaster (BD)

After Disaster 360,253 373,075 N/A 24,676 397,751 758,003 41.6 - 8.0 49.7 (AD)

Note : Abbreviation for transport modes. L=Land transport, R=Railway, S=Ship

3) Average transport cost of modeling solution shows 51% reduction for international cargo

and 31% reduction for domestic cargo than that of condition AD, as shown in Table 10.

Larger reduction gained by international cargo, notably export cargo because of utilization

of ship. Haulage for export container is a lot more than domestic container, but shipping cost

of it is only slightly different with domestic container. The reason is because the export

container treated as domestic container where additional charge is only applicable in the

term of document fee for connecting vessel.

4) After internalization of external cost, modeling result still produces 29% lower total transport cost than that of BDCopy and 38% lower total transport cost than that of AD. Comparing to AD condition only, modeling solution managed to averagely save opportunity

cost for transportation by USD 62.80/TEU. External cost of modeling result also represents

efficiency in transport by optimum deployment of means of transport.

5) Projected emission in ton measurement for all condition in Table 4 is calculated based on

traveled distance of each cargo within the network with respect to utilized mode of transport.

The solution produces 39% and 46% lower CO2 emission than that of AD and BD

respectively. Compared to AD condition, increase in sea transport emission by 6.2 ton is

compensated by reduction of land transport emission by 28.9 ton which shows a beneficial

trade-off of emission by modal shift.

Cost is one of the most important considerations in modal choice and actual competition between modes is primarily depends on serviced distance. Disruptions result in much more transport demand than supply, creating rise of transport cost. Transport cost/unit cost and distance data are plotted on x-axis and y-axis respectively to depict the competition between modes of transport in the modeled network.

Fig.14 (a) shows the influence of transport cost that produced by land and marine transport on door-to-door distance in the applied case, with fixed modes given in Table 5. It shows different cost functions of transport modes according to serviced distance. While land transport has a lower cost function for short distances, its cost function climbs faster than sea transport cost functions after break-even point (BEP) at 85 km.

Land cost function is rather straightforward although there are instability of cost in longer distance because of chain effect from disaster in the form of extra cost. Sea transport cost function climbs in a curve manner. It because of sea transport network is accessible through a terminal that also involves land transport,Copy adds the cost structure with additional component for initial transport to terminal. Container trucking fares are unstable and may easily rise because extra charges, but containership slots are booked in advance at a locked price and cost may remain, even when the system is disrupted

(USD /TEU) y = 0.489x + 46.87 2.0 (USD/TEU/Km) 240 R² = 0.848 220 1.8 200 Land Modes 1.6 180 Sea Modes 1.4 160 1.2 140 120 1.0 100 0.8 Transport cost -0.34 80 costof Unit transport y = 4.746x 0.6 R² = 0.802 60 y = 0.000x2 - 0.129x + 85.61 R² = 0.956 0.4 y = 28.80x-0.77 40 R² = 0.879 20 0.2 (Km) (Km) 0 0.0 0 50 100 150 200 250 300 350 400 450 500 0 100 200 300 400 500 600 700 800 900 1,000 Distance Distance (a) Door-to-door transport cost (b) Unit cost of transport

(USD /TEU) y = 0.489x + 46.87 2.0 (USD/TEU/Km) 240 R² = 0.848 220 1.8 200 Land Modes 1.6 180 Sea Modes 1.4 160 1.2 140 120 1.0 100 0.8 Transport cost -0.34 80 costof Unit transport y = 4.746x 0.6 CopyR² = 0.802 60 y = 0.000x2 - 0.129x + 85.61 R² = 0.956 0.4 y = 28.80x-0.77 40 R² = 0.879 20 0.2 (Km) (Km) 0 0.0 0 50 100 150 200 250 300 350 400 450 500 0 100 200 300 400 500 600 700 800 900 1,000 Distance Distance (a) Door-to-door transport cost (b) Unit cost of transport

Fig. 15 Dependence of transport cost (a) and unit cost (b) on door-to-door distances

Fig. 14(b) shows unit cost of transport decrease as the volume of unit distance increases, which shows the economies of range comparison between the two modes. The unit cost is independent from distance unlike transport cost in Fig. 14(a). Decreasing curves is produced from spreading of fixed unit cost over greater production of distance. It shows the same BEP at

85 km as Fig 5(a). After BEP, more cost reduction can be gained from utilizing sea transport.

BEP is important to be known as decision support in how to improve effectiveness of transportation network in LUSI disaster mitigation.

4.5 SENSITIVITY ANALYSIS

To show the appropriateness of modeling solution to changing transport environment, sensitivity analysis is presented by giving more attention to the following relations:

1) Change in cargo demand and production of transportation by network solution

Fig. 16 shows that increase in cargo demands slightly increased the transport cost if there is

no additional transport supply to handle the cargo.

120 0.00035

100 0.00030

0.00025 80 Copy 0.00020 60 0.00015 40 0.00010 Cost/TEU - Cargo increase Unit Cost Unit (US$/TEU/Km)

Transpprt(US$/TEU) Cost 20 0.00005 Unit Cost- Cargo increase 0 - Current +10% +20% +30% +40% +50% +60% +70% (4600 TEU/day) Increase in Cargo demand (% from current) Fig. 16 Dependence between increased demand and transport cost

The reasons can be explained as container transport market is not in flexible market where prices rises high when there is lack of supply. Analyzed container transport market have several main player, notably shipping companies and freight forwarders and rises in price is rare to be seen except seasonally such as holiday. Shippers are mostly factory and industries where fixed transport price already been negotiated in the beginning of fiscal year. Average unit cost decline in linearly fashion after 20% of increasing in cargo demand because of more utilization of marine based modes than land based mode of transport. Projected CO2 emission from the network commonly increase with more utilization of transport modes as shown in Fig. 17, where emission would increase averagely 0.2% per 1 % increase in cargo demand. Interestingly, efficiency of transport production in both cost and CO2 emission can be seen at 10% increase in cargo demand to conclude that the network solution produce more efficiency with the increase of distributed cargo within network.

60 Land 50 Rail Sea 40 Total Emission 30 Copy 20

CO2Emission(Ton) 10

0 Current +10% +20% +30% +40% +50% +60% +70% (4600 TEU/day) Increase in cargo demand (% from current condition) Fig. 17 Dependence between increased demand and projected CO2 emission

120%

100%

80%

60%

40% Utilization ratioUtilization

20%

0% Current Increase in Cargo demand (%) (4600 +10% +20% +30% +40% +50% +60% +70% TEU/day ) PTW 35% 33% 32% 30% 28% 26% 26% 26% PTT 0% 2% 2% 5% 7% 9% 11% 12% PP 0% 5% 5% 5% 5% 5% 6% 8% Direct to PTP 65% 60% 60% 60% 60% 60% 57% 54% Fig. 18 Dependence between increased demand and utilization of sea port as consolidation point

2) Change in cargo demand and utilization of consolidation point, notably sea ports.

Fig. 18 shows the utilization ratio of consolidation point within network as distributed cargo

increases. Direct shipment to PTP as delivery point shall reduce followed by increased

consolidation point in the port among the network. Interestingly PTW will attract less cargo

after cargo increase more than 30% as cargo consolidate more in other consolidation point

such as PTT in Probolinggo district and PP in Pasuruan district Copy

5 CONCLUSION

In emergency logistic, mitigating unexpected event that follows a disaster will improve emergency response speed. Mitigation model for cargo distribution in a disrupted network is introduced followed with case study in container transportation problem post LUSI disaster.

Proposed mitigation steps provided procedure for planning mitigation of unexpected event related to cargo transportation upon disaster. Result shows that marine system assistance manages to reduce total transport cost in mitigating land based disaster by cargo consolidating policy and intermodal transportation. Some advantages behaviors that minimize total transport costs were found after mitigation steps conducted and distribution of cargo was computationally simulated, i.e.:

1) Cargo consolidation policy reduced total transport cost compared to direct transport.

Marine transport have more advantage in consolidating policy as it could capture

large volume of cargo and transport with efficient manner to delivery point.

2) Intermodal transportation gave more choices in creating alternative routes that leads

to better solution for distribution problem. With more alternative been explore chance

for creating efficient network will be higher. 3) Total emission can be reducedCopy by utilization of larger means of transport and shows solution advantage in the effort to reduce emission impacts in sustainable freight

distribution.

4) Assumption for modeling intermodal transportation network is very important as it

involved combination of transport mode and terminal, in which will produce

efficiency in one place and efficiency in another place. Trade-off between variable

will determine the overall efficiency of created network.

ACKNOWLEDGEMENTS

First and foremost, the author would like to thank to supervisor of this master thesis, Prof.

Takeshi Shinoda for the valuable guidance and advice and inspired the author greatly to work in this thesis. Besides, the author would like to thank the member of the Committee of Professors from

Department of Naval Architecture and Marine Systems Engineering (NAMS) at Kyushu University for providing a good environment, lectures and facilities which many are useful reference to complete this thesis.

Special thanks for lecturers and colleagues at Institut Teknologi Sepuluh Nopember Surabaya

(ITS), Dr. Tri Achmadi, Dr. Setyo N., Mr. Firmanto H., Mr. Sumanta Buana, Mr. Agus A., Ms. Niluh

P., Mr. Ferdhi Z. and Mr. Irwan Y., for their valuable guidance and suggestion and the staff of PT.

Samudera Shipping Services – Container Shipping Division for all the provided information related to this thesis. Needless to mention is Mr. Dani Mintaraga, Former Managing Director, who had been a source of inspiration for author’s career in container shipping industry.

Also, the author would like to take this opportunity to thank to Mr. Amry Dasar, Ms. Nguyen Thi

Hoa Ha, Mr. Luky H., Ms. Lutfiana A., Ms. Tri Wulaningsih, Mr. Syamsul H. and Mr. Nugroho S. for wonderful friendship, functional design lab mates (小林晃大氏、瀬瀬さおり氏、高松佑輔氏、神保昌史氏、中尾智彦氏、豊永祐太氏、松本拓久氏、三笠亮氏、山本眞也氏、羅涛氏Copy ), seniors (木村孝司氏、下川和広氏、片山智博氏、田辺航輝氏、豊田遼英氏、松崎健悟氏、王

明い氏、松田和貴氏, 橋本勝氏, 鵜池健氏) and all friends that cannot be mention all for their help and wishes for the completion of this thesis.

Finally, yet importantly, the author would like to express heartfelt thanks to beloved parents Dr. I

Made Yasa and Ms. Diantariningsih for their blessings and beloved brother Mr. Gita Raditya for all the support, Completion of this thesis and master course within 1 year is a personal achievement for the author and it would not be achievable without all the help of these wonderful individuals.

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Copy

APPENDIX A: MEASURED INFRASTRUCTURE AND DEMAND DATA

OF PROSPECTIVE CONSOLIDATION POINTS

Supply Measured Consolidation Point Port Infrastructure

Berth long Terminal Storage No. Name of Node LWS (m) Yard (m2) (m) Area (m2) area (m2)

1 Benoa (BALI) 646 9 1,300 1,906 14,722 2 Bima (NTB) 242 6 2,870 400 2,000 3 Celukan Bawang (BALI) 368 17 1,796 810 10,250 4 Ende (NTT) 175 9 2,495 2,000 4,500 5 Kupang (NTT) 373 9 6,205 2,500 2,465 6 Lembar (NTB) 453 6 4,555 720 12,750 7 Maumere (NTT) 180 9 2,700 4,500 3,850 8 Pasuruan (EAST JAVA) 1,703 2 1,676 7,709 12,225 9 Probolinggo (EAST JAVA) 1,044 2 3,828 19,159 8,000 10 Tg. Perak (EAST JAVA) 10,624 11 30,000 281,251 109,769 11 Tg. Wangi (EAST JAVA) 769 12 10,000 8,050 34,464 Total 16,577 91 67,425 329,005 214,995 Max 10,624 17 30,000 281,251 109,769 Min 175 2 1,300 400 2,000 Range 10,449 16 28,700 280,851 107,769 Average 1,507 8 6,130 29,910 19,545

Demand Measured Consolidation Point Shipping Shippers Land Area (N*GT* Population Population No. Name of Node (N) Cargoes (Ton) (Prefecture) 10,000 Ton) (province) (Person) Km2 1 Benoa (BALI) 4,609,036 10,778 333,372 3,479,800 5,780.06 383,880 2 Bima (NTB) 200,585 1,585 349,014 4,429,500 18,572.32 419,302 3 Celukan Bawang (BALI) 22,777 434 770,619 3,479,800 5,780.06 650,237 4 Ende (NTT) Copy - - 800,000 4,448,900 48,718.10 238,195 5 Kupang (NTT) 1,006,339 2,647 387,647 4,448,900 48,718.10 299,518 6 Lembar (NTB) 81,994 863 143,320 4,429,500 18,572.32 816,523 7 Maumere (NTT) 89,619 914 962,366 4,448,900 48,718.10 279,464 8 Pasuruan (EAST JAVA) - - 200,000 36,895,600 47,799.75 177,000 9 Probolinggo (EAST JAVA) 637,503 6,460 552,700 36,895,600 47,799.75 186,778 10 Tg. Perak (EAST JAVA) 80,886,510 14,492 13,953,673 36,895,600 47,799.75 3,282,156 11 Tg. Wangi (EAST JAVA) 305,649 1,673 1,184,914 36,895,600 47,799.75 1,540,000 Total 87,840,010 39,846 19,637,625 Max 80,886,510 14,492 13,953,673 Min - - 143,320 Range 80,886,510 14,492 13,810,353 Average 7,985,455 3,622 1,785,239

APPENDIX B: GENERATED TRANSPORT ROUTES

JAVA SEA Land Transportation Alternatives: •Malang – Pasuruan– Surabaya (highway)N TUBAN •Malang – Pasuruan– Mojosari – Surabaya C MADURA ISLAND •Malang – PasuruanKalianget– ( SumenepBanyuwangi ) E GRESIK N •Malang – Surabaya (highway) Port of Tanjung Perak T Babat Branta (Pamekasan) LAMONGAN •Jember – Pasuruan– Banyuwangi R BOJONEGORO A SURABAYA •Jember – Pasuruan– Surabaya L •NTB/NTT- Bali - Banyuwangi- Surabaya MADURA STRAIT J NGAWI MOJOKERTO Mojosari Gempol A Japanan SITUBONDO Bangil V Caruban NGANJUK JOMBANG A Pandaan PASURUAN Maospati Kertosono

MADIUN Purwosari PROBOLINGGO

MAGETAN BONDOWOSO KEDIRI Lawang Ketapang Meneng MALANG JEMBER PONOROGO BLITAR TRENGGALEK LUMAJANG BANYUWANGI Rambipuji Gilimanuk TULUNGAGUNG BALI ISLAND Puger PACITAN Benoa INDONESIAN OCEAN

Fig.B- 1 Generated land transport routes to mitigate LUSI disaster

JAVA SEA

N TUBAN

C MADURA ISLAND Kalianget ( Sumenep ) E GRESIK N Port of Tanjung Perak T Babat Branta (Pamekasan) LAMONGAN R BOJONEGORO A SURABAYA L

SIDOARJO MADURA STRAIT J Sea TransportationNGAWI MOJOKERTO Mojosari Gempol A Japanan SITUBONDO Bangil V Alternatives: Caruban A NGANJUK JOMBANG CopyPandaan PASURUAN •PasuruanMaospati - SurabayaKertosono •ProbolinggoMADIUN - Surabaya Purwosari PROBOLINGGO MAGETAN BONDOWOSO •Banyuwangi - SurabayaKEDIRI Lawang •Probolinggo - Banyuwangi Ketapang Meneng MALANG JEMBER PONOROGO •Pasuruan - Banyuwangi BLITAR TRENGGALEK LUMAJANG BANYUWANGI Rambipuji Gilimanuk •Probolinggo-PasuruanTULUNGAGUNG BALI ISLAND Puger PACITAN Benoa INDONESIAN OCEAN

Fig.B- 2 Generated coastal shipping transport routes to mitigate LUSI disaster

JAVA SEA

N TUBAN

C MADURA ISLAND Kalianget ( Sumenep ) E GRESIK N Port of Tanjung Perak T Babat Branta (Pamekasan) LAMONGAN R BOJONEGORO A SURABAYA L

SIDOARJO MADURA STRAIT J NGAWI MOJOKERTO Mojosari Gempol A Japanan SITUBONDO Bangil V Caruban NGANJUK JOMBANG A Pandaan PASURUAN Maospati Kertosono

MADIUN Purwosari PROBOLINGGO

MAGETAN BONDOWOSO Sea Transportation Alternatives:KEDIRI Lawang •Bali - Pasuruan - Surabaya Ketapang Meneng MALANG JEMBER PONOROGO BLITAR •Bali - ProbolinggoTRENGGALEK - Surabaya LUMAJANG BANYUWANGI Rambipuji Gilimanuk •Bali – Surabaya TULUNGAGUNG BALI •Bali – Banyuwangi ISLAND Puger PACITAN •Bali – Banyuwangi - Surabaya Benoa •Bali - Pasuruan - Surabaya INDONESIAN OCEAN

Fig.B- 3 Generated interisland shipping transport routes to mitigate LUSI disaster (1)

JAVA SEA

N TUBAN

C MADURA ISLAND Kalianget ( Sumenep ) E GRESIK N Port of Tanjung Perak T Babat Branta (Pamekasan) LAMONGAN R BOJONEGORO A SURABAYA L

SIDOARJO MADURA STRAIT J NGAWI MOJOKERTO Mojosari Gempol A Japanan SITUBONDO Bangil V Caruban NGANJUK JOMBANG A Pandaan PASURUAN Maospati Kertosono Sea TransportationMADIUN Alternatives: Purwosari PROBOLINGGO MAGETAN Copy •NTB/NTT - Pasuruan - Surabaya BONDOWOSO KEDIRI Lawang •NTB/NTT - Probolinggo - Surabaya Ketapang Meneng MALANG JEMBER PONOROGO •NTB/NTT - Bali – SurabayaBLITAR TRENGGALEK LUMAJANG BANYUWANGI •NTB/NTT- Bali - Banyuwangi Rambipuji Gilimanuk TULUNGAGUNG •NTB/NTT- Bali - Banyuwangi - Surabaya BALI ISLAND Puger •NTB/NTTPACITAN - Surabaya Benoa INDONESIAN OCEAN

Fig.B- 4 Generated interisland shipping transport routes to mitigate LUSI disaster (2)

JAVA SEA

Rail Transportation Alternatives: N TUBAN •Banyuwangi – Pasuruan – Malang – C MADURA ISLAND Tulungagung – SurabayaKalianget ( Sumenep ) E GRESIK N •Banyuwangi – Pasuruan – Surabaya Port of Tanjung Perak T Babat Branta (Pamekasan) LAMONGAN R BOJONEGORO •Malang - Pasuruan– Surabaya

A SURABAYA TURI STATION L Surabaya Container Depot

MADURA STRAIT J NGAWI MOJOKERTO Mojosari Pasuruan A Japanan Container SITUBONDO Gempol Depot V Caruban PASURUAN STATION A NGANJUK JOMBANG Pandaan PASURUAN Maospati Kertosono PROBOLINGGO STATION

MADIUN Purwosari PROBOLINGGO

MAGETAN BONDOWOSO KEDIRI Lawang Ketapang Meneng MALANG JEMBER PONOROGO BLITAR TRENGGALEK LUMAJANG BANYUWANGI Rambipuji Gilimanuk Container TULUNGAGUNG Depot BALI ISLAND Puger PACITAN Benoa INDONESIAN OCEAN

Fig.B- 5 Generated freight train transport routes to mitigate LUSI disaster

Copy

APPENDIX C: EXAMPLE CASE TO MEASURE THE ALGORITHM

ABILITY TO DELIVER OPTIMUM SOLUTION

TRANSPORTATION PROBLEM (EXAMPLE OF DISTRIBUTION) Case 25 Problem Definition 7 dockyard in a region need to be supplied with steel plates and there are 3 factories available with some plates as follows:

Dockyard Demand for plates Factories Supply availability D1 14 tons F1 28 tons D2 12 tons F2 35 tons D3 17 tons F3 32 tons D4 11 tons 95 D5 9 tons D6 11 tons D7 21 tons D8 tons 95

Transportation costs Cost W1 W2 W3 W4 W5 W6 W7 F1 3 4 7 10 4 6 3 F2 7 15 2 5 7 9 14 F3 12 5 12 16 4 5 9

Q: what is the minimum total transportation cost to satisfied supply and demand of steel plates A: Given Transportation problem in matrices Transportation Costs Dockyard D1 D2 D3 D4 D5 D6 D7 D8 Factory F1 3 4 7 10 4 6 3 0 28 F2 7 15 2 5 7 9 14 0 35 95 F3 12 5 12 16 4 5 9 0 32 14 12 17 11 9 11 21 0 95

Problem solving using solver add-ins supply and demand flows warehouses D1 D2 D3 D4 D5 D6 D7 D8 total factories F1 7 0 0 0 0 0 21 28 F2 7 0 17 11 0 0 0 35 F3 0 12 0 0 9 11 0 32 total 14 12 17 11 9 11 21 0

MIN TOTAL COST = 373 Problem solving using proposed algorithm Copy supply and demand flows warehouses D1 D2 D3 D4 D5 D6 D7 D8 total factories F1 7 0 0 0 0 0 21 28 F2 7 0 17 11 0 0 0 35 F3 0 12 0 0 9 11 0 32 total 14 12 17 11 9 11 21 0

MIN TOTAL COST = 373

TRANSPORTATION PROBLEM (EXAMPLE OF DISTRIBUTION) Case 2 7 Problem Definition 7 dockyard in a region need to be supplied with steel plates and there are 6 factories available with some plates as follows:

Dockyard Demand for plates Factories Supply availability D1 34 tons F1 28 tons D2 22 tons F2 37 tons D3 45 tons F3 32 tons D4 32 tons F4 35 tons D5 34 tons F5 46 tons D6 24 tons F6 33 tons D7 20 tons 211

211

Transportation costs Cost W1 W2 W3 W4 W5 W6 W7 F1 25 10 7 9 9 12 12 F2 10 13 9 12 7 5 6 F3 13 6 5 6 9 12 8 F4 29 12 6 4 12 12 12 F5 9 13 6 9 13 10 8 F6 23 4 10 12 5 8 9

Q: what is the minimum total transportation cost to satisfied supply and demand of steel plates A: Given Transportation problem in matrices Transportation Costs Dockyard D1 D2 D3 D4 D5 D6 D7 D8 Factory F1 25 10 7 9 9 12 12 28 F2 10 13 9 12 7 5 6 37 211 F3 13 6 5 6 9 12 8 32 F4 29 12 6 4 12 12 12 35 F5 9 13 6 9 13 10 8 46 F6 23 4 10 12 5 8 9 33 34 22 45 32 34 24 20 0 211

Problem solving using solver add-ins supply and demand flows warehouses D1 D2 D3 D4 D5 D6 D7 D8 total factories F1 0 0 5 0 23 0 0 28 F2 0 0 0 0 0 24 13 37 F3 0 0 32 0 0 0 0 32 F4 0 0 3 32 0 0 0 35 F5 34 0 5 0 0 0 7 46 F6 0 22 0Copy 0 11 0 0 33 total 34 22 45 32 34 24 20 0

MIN TOTAL COST = 1281

Problem solving using proposed algorithm supply and demand flows warehouses D1 D2 D3 D4 D5 D6 D7 D8 total factories F1 0 0 5 0 23 0 0 28 F2 0 0 0 0 0 24 13 37 F3 0 0 32 0 0 0 0 32 F4 0 0 3 32 0 0 0 35 F5 34 0 5 0 0 0 7 46 F6 0 22 0 0 11 0 0 33 total 34 22 45 32 34 24 20 0

MIN TOTAL COST = 1281

Graduate School of Engineering, Department of Marine System Engineering, Kyushu University, Japan

Surabaya

Banyuwangi-

Surabaya

Probolinggo-

x x x x

x x x x x

x x x x

SURABAYA

x x

Pasuruan-Surabaya

(Using SPCB) (Using

(Trailer)

Surabaya

x x x x x x x x x x

unit (Trailer)

TEUS

x x

800

265

Sea [S]Sea 2 unit SPCB) (Using

Land [L] Land

Rail [R]Rail 3 unit

Capacity Constraint (number of vehicle available) vehicle of (number Constraint Capacity

Sea [S] Sea

Land [L]Land 1

Rail [R] Rail

Capacity Constraint per 1 vehicle per Constraint Capacity

x x x

S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29

Probolinggo-Banyuwangi xxxxxxxxxxx

BANYUWANGI Copy

x x x

Pasuruan-

Banyuwangi

L R S L L-R L R L-R L-S S-R S-L R-L L-R L R S L R S S-R S-L R-L L-R S-R S-L L-R S S-R S-L

x x x

S1 S2 S3 S4 S5 S6 S7 S8 S9

APPENDIX D : ACCESSABILITY OF TRANSPORT NETWORK BETWEEN CORRIDORS IN MEASURED CASE MEASURED IN CORRIDORS BETWEEN NETWORK OF TRANSPORT ACCESSABILITY : APPENDIXD

Node

Supply/Demand

Number of Column (Supply side) = 29 = side) (Supply Column of Number

Number of Row (Demand side)= 6 side)= (Demand Row of Number

Note : Note

6 Malang

5 Probolinggo

4 Pasuruan

3 Banyuwangi

2 NTB

1 Bali

Summary : Summary

Blank : Not applicable and don’t have transportation cost transportation have don’t and applicable Not : Blank

X : Applicable for transportation network : transportation X for Applicable

S : Sea

R :R Railway L :L Land