University advisors

Computer Science

Thesis no: MCS-2006:18

January 2007

An Optimization Model for Sea Port

Equipment Configuration

Case study: Karlshamn-Klaipeda Short Sea Shipping Link Author: Gideon Mbiydzenyuy

Department of

Interaction and System Design

School of Engineering

Blekinge Institute of Technology

Box 520

SE – 372 25 Ronneby

Sweden

This thesis is submitted to the Department of Interaction and System Design, School of Engineering at Blekinge Institute of Technology in partial fulfilment of the requirements for the degree of Master of Science in Computer Science. The thesis is equivalent to 20 weeks of full time studies.

Contact Information:

Author

Gideon Mbiydzenyuy

E-mail: gmby79@https://www.wendangku.net/doc/5b4592985.html,

University advisors:

Dr. Jan A. Persson Dr. Lawrence E. Henesey

jan.persson@bth.se larry.henesey@bth.se

Department of Systems and Software Engineering

Department of

Interaction and System Design Blekinge Institute of Technology Box 520

SE – 372 25 Ronneby

Sweden Internet : www.bth.se/tek Phone : +46 457 38 50 00 Fax : + 46 457 102 45

Abstract

Today, freight volumes on roads have gone up to a level that there is a need for alternative transport modes. Short Sea Shipping (SSS) is one alternative with a potential that can help reduce the high traffic on roads. Most SSS systems use vessels whereby cargo is rolled on and off using a ramp with very small capacities usually less than 500 TEU, but with increasing cargo traffic, it is not clear if such solutions will be efficient. For ports involved in SSS to meet up this new wave of change, the challenge to make appropriate investments and analysis tools is important. The type of vessel suitable for a SSS operation (such as roll-on roll-off (RoRo), lift-on lift-off (LoLo) etc) has been addressed in this thesis based on their compatibility and cost effectiveness with the terminal equipments

The purpose of this study is to develop an optimization model that can be incorporated into a Computer Decision Support System (DSS) for selecting equipments including ships at a strategic level for investments in handling unitised cargo at port terminals in the context of Short Sea Shipping (SSS). The main contribution of the thesis is the application of computer science techniques in the domain of strategic decision making related to the configuration of complex systems (e.g. interrelationships between ships and equipments) with choices of handling equipment.

From modelling the selection of port terminal equipments for SSS, we realised that while integer linear programming is a promising approach for studying such systems, it remains a challenge to handle complex issues in depth especially in relation to the quay crane due to interdependencies between time, cost and capacity of equipments. Model results indicates that a LoLo vessel with a capacity between (500 and 1000 TEU) capable of completing a SSS voyage such that handling is done within 48 hours will be less costly than a RoRo that does it with multiple voyages or one voyage each for multiple RoRo vessels for TEU volumes greater than 1000. But RoRo vessels remain useful for trailers that cannot be transported by LoLo vessels.

Keywords: modelling, optimization, handling equipments, containers.

Acknowledgements

I am deeply indebted to my supervisors Dr. L. Henesey and Dr Jan Persson, who did not only gave me the opportunity to share their great ideas, but helped me acquire the necessary impetus to be part of the entire academic research community. Their commitment kept me motivated and inspired.

I am most grateful to the port of Karlshamn in Sweden, port of Klaipeda in Lithuania, DFDS Tor Line and the East West Research Project Team, for creating a conducive environment necessary to carry out this research work.

To my fellow student colleagues at BTH, not forgetting my network of friends worldwide, I must say it has been a unique moment of my life, sharing different ideas from all around the world has added value to my education.

Without the love and kindness of the Swedish people within the Karlshamn neighbourhood, life outside academic cycles would not have been so enriching, notably, my sincere gratitude goes to the entire H?glund family and a host of several other families, I most say thanks for their constant motivation.

I feel a deep sense of gratitude for my entire family, particularly my dad and mum, for their relentless efforts to keep me on the right track for the whole of my life.

And finally to the Almighty God, the architect of all creation, for enabling me once more to cross this junction, I remain thankful.

The journey is long…

but he who asked, never get missing!!!

Grace Suinyuy (1992).

CONTENTS

1 Introduction (9)

1.1 Background of theThesis (9)

1.1.1 Some Foot Prints of Globalisation (9)

1.1.2 Problem Background (10)

1.2 Scope of the Study (12)

1.3 Purpose of the Study (12)

1.4 Discussion of the Problem (12)

1.5 Research Questions (13)

1.5.1 Main Problem (13)

1.5.2 Sub Problems (13)

1.6 Lay out of the Thesis (13)

2 Research Design (14)

2.1 Introduction (14)

2.2 An Overview of the Research Method (14)

2.3 Research Design (14)

2.4 Data and Information Collection (15)

2.4.1 Secondary Data Collection (15)

2.4.2 Primary Data Collection (15)

2.5 Research Evaluation (16)

2.5.1 Evaluation (16)

3 The Structure of SSS; A Problem Perspective (17)

3.1 How is SSS Defined? (17)

3.2 What are the Current Problems Associated With SSS? (18)

3.2.1 The Cost Dimension for SSS (18)

3.2.2 The Capacity Dimension for SSS (20)

3.3 What are the Requirements Imposed by SSS Practices on Sea Ports? (22)

4 Terminal Handling Technologies for SSS (23)

4.1 Literature Review (23)

4.1.1 What are the Current Trends toward Handling Problems for SSS? (23)

4.1.2 ICT Technology and the Marine Industry-Case of Ports and Terminals (24)

4.2 Handling Systems for Port Terminals (25)

4.2.1 A Literature Review of Handling Systems; Categorisations (26)

4.2.2 Handling Systems and Performance Measure (27)

4.3 The Concept of Automated Handling (28)

4.3.1 Automated Handling Equipments (29)

4.3.2 Non-automated Handling Equipments (30)

5 Karlshamn-Klaipeda Case Study (33)

5.1 The Case Study (33)

5.2 Port of Karlshamn – Sweden (34)

5.2.1 General Description (34)

5.2.2 Infrastructure and Port Equipment (35)

5.3 Port of Klaipeda – Lithuania (35)

5.3.1 General Description (36)

5.3.2 Infrastructure and Equipments (36)

5.4 Marine Leg Karlshamn-Klaipeda (37)

6 Model Development (39)

6.1 Mathematical Modelling Techniques (39)

6.2 Integer Linear Programming Optimization Model (ILP) for Port Terminals (41)

6.3 Model Description (42)

6.3.1 ILP Fundamental Assumptions (42)

6.3.2 Decision Variables (43)

6.3.3 Objective (43)

6.3.4 Constraints (44)

6.4 ILP Mathematical Formulation (44)

7 Results and Analysis (47)

7.1 ILP Input Data (47)

7.2 ILP Model Configuration (47)

7.3 ILP Model Output (48)

7.4 Output Analysis (50)

7.5 Sensitivity Analysis (54)

7.6 Research Limitations (55)

8 Conclusion and Future Work (56)

8.1 Conclusion (56)

8.2 Future Work (57)

9 Bibliography (58)

Appendix Glossary of Container Terminals Used in this Thesis (61)

Figure 2-1 OR Process Radon (2000) (15)

Figure 4-1 Displacement time graphs for vertical and horizontal ship reloading systems (26)

Figure 4-2 A fleet of fully Automated Guided Vehicles transporting containers (29)

Figure 4-3 RTG with GPS system (30)

Figure 4-4 Mafi Platform showing position for securing a tugmaster (31)

Figure 4-5 Rail Mounted Gantry (RMG) crane (31)

Figure 4-6 Kalmar CSC Straddle Carrier (31)

Figure 4-7 Side view FLT's lifting a container (32)

Figure 5-1 Karlshamn-Klaipeda link over the Baltic Sea (33)

Figure 5-2 Growth in RoRo container traffic for the port of Karlshamn 2001 to 2005 (35)

Figure 5-3 Container growth, port of Klaipeda 1997-2006 (36)

Figure 5-4 Ship’s traffic over the Baltic Sea and the Karlshamn Klaipeda Shipping link (38)

Figure 5-5 Forecast of freight growth units in the Karlshamn-Klaipeda link (38)

Figure 6-1 Simple generic ILP Model represented as a tree (42)

Figure 7-1 Variation in number of ships with TEU demand for a 24 Hour handling (51)

Figure 7-2 Variation in number of ships with TEU demand for a 48 hour handling (51)

Figure 7-3 Variation in number and type of ships with yard vehicles (52)

Figure 7-4 Variation of yard vehicles with TEU Volume for different handling periods (52)

Figure 7-5 Cumulative reuse of equipments fork lift, yard vehicles and quay crane (53)

Figure 7-6 Investment point in ships with demand for different TEU volumes (53)

Figure 7-7 Total cost per TEU variation with TEU demand (54)

Table 3-1 Cost estimates for RoRo Vs LoLo (19)

Table 5-1 Some handling equipments in the port of Karlshanm (35)

Table 5-2 Some handling equipments used in the port of Klaipeda (37)

Table 7-1 Model parameters (48)

Table 7-2 Out Put results with Handling Time Window = 30 Hours (49)

Table 7-3 Output results with Handling Time Window = 48 Hours, (49)

Table 7-4 Estimated number of trucks and train capacity (50)

Table 7-5 Analysis of model sensitivity (55)

Chapter 1

1Introduction

This section introduces the thesis work. It begins with a motivation, purpose and discussion of the research problem. The research question, scope and layout of the work are presented.

1.1Background of the Thesis

The purpose of this study is to develop an optimization model that can be incorporated into a Computer Decision Support System (DSS) for selecting equipments at a strategic level for investments in handling unitised cargo at port terminals in the context of Short Sea Shipping (SSS). In the simplest of terms SSS is the transport of passengers or cargo by water without crossing an ocean. The type of vessel suitable for a SSS operation (such as RoRo, LoLo etc) has been addressed based on the compatibility and cost effectiveness with the terminal equipments. We consider unitised cargo, in comparing handling systems based on the cost, and capacity vectors. We therefore combine all these and model as a single system in order to formulate an integer linear optimization model (ILP) as a first step toward realising a decision support tool for terminal handling.

In trying to model equipments selection for the entire container terminal a large number of constraints and variables needs to be taken into account. The challenge with modelling such a system is that constraints and variables have interdependencies with logical connections that require intuition. By modelling different problem segments as sub systems, and solving in an integrated model, such difficulties can be minimised.

The thesis contributes to the modelling of equipments in container terminals using the linear programming method of optimization, building the body of knowledge on unitised terminal handling performance, especially in the interdisciplinary area of logistics, computer science, operational research and engineering. The thesis is developed from an applied computer science perspective. The main contribution of the thesis is the application of computer science techniques in the domain of strategic decision making related to the configuration of complex systems (e.g. interrelationships between ships and equipments) with choices of handling equipment.Such an approach could be useful in applying computer science to model similar areas such as cargo handling in the air plane industry, facility usage for industrial production planning processes, product life cycle along a supply chain etc. An integrated approach for the distributed segments of a transport chain will cumulate into a synergic cohesion of the entire chain. This is of relevance in integrating the supply chain which needs to continuously adjust in order to coup with increasing globalization.

1.1.1Some Foot Prints of Globalisation

The dawn of the 21st century has ushered in new business opportunities alongside new challenges. The discrepancies separating national economies have received significant attention under the umbrella of globalization with the international community mobilising resources to alleviate the underprivileged nations of the world. There is so much trade in the world today especially between developed or “privileged” nations, that is exacerbating the need for new techniques. Changes in the structure of the global economy, e.g., out-sourcing, Just-in-time, more focus on supply chain

management, etc and the ever widening free trade zones resulting from the dissolution of national boundaries (e.g. EU, EFTA, and NAFTA etc) has led to increasing needs for superior logistic solutions. Real time logistic solutions are increasingly becoming an important issue on many business agendas due to such practices as Just-In-Time (JIT), Supply Chain Management (SCM) etc.

Within Europe, geographic conditions, (as in the UK) cause them to be dependent on shipping for trade. For EU member states SSS is increasingly becoming an important mode of transport e.g. over three-quarters of the unitized cargo shipped in the UK today is by, bound for, or originated from short sea. The use of intermodal transport networks is approximately 70% road, 20% rail and about 8% SSS and the rest distributed between pipeline and air1. This indicates that the potential of SSS is yet to be fully exploited. The evolution of international shipping since the introduction of the container has affected ports than any other terminal. The marine side is increasingly creating a capacity imbalance with the land side, adding more problems to handling operations. Consequently, SSS is gaining much attention due to its potential use in alleviating some of the problems of congestion, pollution, increasing cargo volumes, and related negative environmental impacts as a result of road transport.

1.1.2Problem Background

Numerous challenges (bottle necks) have been encountered in suggesting SSS solutions e.g. the lack of intermodal liability regime, unequal distribution of incentive measures, lack of comparable statistical data on SSS and other issues in relation to vessels speed and capacity, up to terminal handling and management issues2. Managing cargo flows between ports and inland destinations has remained a challenge for terminal operators (Chadwin & Talley, 1990, Notteboom, 1997). Often, terminal operators need to overcome congestion issues when serving huge ships loaded with thousands of containers. The ships have huge capacities that are reaching nearly 10,000 TEU+ whereas the capacities of trucks are between 1-2 TEU. This situation is fast becoming a common occurrence in many parts of the world. For shippers, delay in ports means rising costs, adding to customer pressure for goods to be delivered just in time. Clearly, there is much attention on terminal operators to solve these issues. In order to reach their goals or objectives many terminal operators need technical assistance when selecting handling equipments from a strategic level (for investments), tactical and operational level (for deployment) in order to handle their operations. This necessitates the use of computer support in managing operations.

Numerous books and scientific articles have been written about managing problems in a container terminal. So far; M. Katta et al (2005), presents a Decision Support System for operations in a container terminal, and discusses the mathematical models and algorithms used in designing the DSS.

A literature survey of container terminal problems by H. Rashidi & E. P. K. Tsang (2005) indicates that most problems in a container terminal can be formulated as constraint satisfaction and optimization problems. The conclusion from the study is that most container terminals place a high level of importance on efficient use of their terminal equipment. One important decision that terminal managers must consider is the efficient allocation of quay cranes so as to satisfy a ship time-window or minimize the waiting times of ships in the port. The decision on whether to invest on quay cranes or not becomes even more critical, especially when comparing the handling performance of a quay crane and its impact on the entire handling process with the cost of investing in a quay crane. An Intelligent Decision Support System for crane scheduling using optimization techniques has been proposed by Guohua WAN (March 2004). This study indicates that the operations of a quay crane needs significant attention from terminal management in order to evaluate their handling capability well in advance to make intelligent investment decisions.

1EU Director General Report 2005

Techniques of simulation and optimization along with other Information and Communication Technology (ICT) related tools have offered significant contributions in addressing some problems in container terminals (H.O. Gunter and K.H. Kim; 2005). A mix integer based optimization approach to study the interactions between Quay Cranes (QCs), Automated Guided Vehicles (AGVs) and Automated Yard Cranes (AYCs) in an integrated model by Henry et al. (2005) indicates that a single optimization model can be rather complex when considering different problems due to problem variation and constraints in a container terminal. Therefore one has to consider few but sufficient issues to address in an integrated model. An attempt to an integrated study of scheduling various types of equipments in an automated container terminal using a beam search heuristics algorithm has been provided by P. J. Meersmans & A. P. M. Wagelsmans (2001). The study shows that a long term planning horizon for scheduling problems is important so as to take account of information updates. Given another look one may say scheduling problems should be addressed from the point of an investment decision, to take account of optimising facilities. The technical performance of RoRo vs. LoLo has been compared (J. Igeilska; June 1996) leading to a RoRo preferred choice. Little attention has been given to the comparison of their performance with respect to the handling configuration at terminals. J. A. Ottjes et al (May 2006) has come up with a proposed generic simulation model structure for the design and evaluation of multiterminal systems for container handling which has been applied to the Rotterdam port terminals. The simulation helped determine the handling requirements for deep-sea quay lengths, storage capacities, as well as equipments for interterminal transport systems

A similar study carried out by S.Francesc et al (2006), to analyze the internal transport subsystem in a marine container terminal and investigates the effect of the type of handling equipment used, came up with the conclusion that “assignment of the handling equipment resources to an individual wharf crane in a particular berth is not advisable, since any decentralized decision system involves more resources. The handling equipment resources must be assigned to the berth as a whole to obtain greater efficiency, but then a focus on operation planning and reliability is required”.

From the above studies it can be seen that a lot of research work has been carried out in developing models of container terminal operations that are or can be incorporated into DSSs. Most of the studies indicate that it is difficult to model the entire container terminal in a single integrated optimization model. Consequently, most of the studies have focused on developing models to solve individual problems related to specific terminal equipments (especially the quay crane) and not integrated or combine problems relating all handling equipments. On the other hand L. M. Gambardella and A. E. Rizzoli (2000) argue that it is necessary for the terminal yard and quays to be managed in an integrated fashion i.e. with simultaneous regard for parallel processes. D. Steenken et al (2004) identifies the need to focus on investigating ‘integrated optimization’ problems since few studies as at now have addressed ‘integrated problems’ with an optimization perspective.

Such problems, as selecting which equipments to invest on or to deploy, may need to be approached from an integrated perspective since they concern the entire terminal. The decision about which equipments to invest on may not be difficult for terminals handling small volumes of cargo. However such a decision can be difficult to consider for different types of equipments, with increasing cargo volumes and stricter customer requirements, when several factors, such as congestion, performance, safety etc needs to be taken into accounts. Since the decision about selecting which handling equipments to use have a Boolean (true or false) character, this makes such a system suitable for modelling with an Integer based optimization model. To make use of advanced optimization tools (e.g. CPLEX), the model can be formulated using linear relationships, thus we model the entire system using Integer Linear Optimization Model (ILP).

With ILP based models, the modelling task can greatly be reduced since ILP models have a natural and accurate representation of many practical optimization problems (R. Fourer et al; 2003). Partly due to their simplicity, and partly because of their potential in arriving at solutions for problems with a multitude of constraints (of the order of hundreds), container terminals can be a land of

opportunities for applying ILP based models. One important thing to consider in the use of such models is their high computer memory resource demand.

An ILP model for selecting handling equipments will enable port terminals such as Karlshamn and Klaipeda to preview what kind of handling tools shall be required as freight volumes increases at a strategic level. Based on demand forecast the tool can be suitable for choosing handling systems to deploy at tactical level.

1.2 Scope of the Study

The study is focused on terminal operators as well as shipping lines involved in SSS practices. We hope to improve their understanding of the effects of cost, capacity and time on the different handling systems employed. In particular, we will focus on two ports in the context of a case study. The port of Kalshamn, situated in the South East of Sweden, west of the Baltic Sea, and the port of Klaipeda that lies along the Eastern coast of the Baltic Sea, south of Lithuania, considered an important link involved in SSS between the east and the west in the Baltic Sea Region.

In addition to a systematic literature review on SSS and handling systems that has been carried out, the goal of the study has also been to use appropriate optimization tools and techniques to address a “real-life” practical problem. Thus relevant assumptions necessary to apply these techniques have been made in developing the model following the Operational Research process (L. Radon 2000) while minimising the gap from physical reality.

1.3 Purpose of the Study

The purpose of this study is to develop an optimization model that can be incorporated into a Computer Decision Support System (DSS) for selecting equipments used in handling unitised cargo at port terminals in the context of Short Sea Shipping (SSS). The type of vessel suitable for a SSS operation (such as RoRo, LoLo etc) has also been addressed based on the compatibility and cost effectiveness with the terminal equipments. We consider unitised cargo, in comparing handling systems based on the cost, and capacity vectors. We therefore formulate this in an optimization model as a first step toward realising a decision support tool for terminal handling.

It is our aim to model and represent the process of selecting terminal equipments including choice of ships in a unitised3 cargo terminal (ports of Karlshamn and Klaipeda) as an ILP based model to exploit the advantages of such models (e.g. ease of real world representation). The goal is to develop a model which can be incorporated into a decision support tool that can assist terminal managers in selecting handling equipments. Based on this model we expect to be able to compare the performance of different handling systems by considering their cost and capacity. Based on the performance of the handling systems we expect to be able to suggest suitable shipping systems between LoLo and RoRo for SSS at different capacity levels.

1.4Discussion of the Problem

The operations in a unitised terminal can basically be classified into three main phases; loading/unloading of ships by Quay cranes, transfer of cargo units to and from yard area (primary and secondary) between yard crane and quay crane and the handling of inbound/outbound cargo units between yard cranes and external trucks and train. The cyclic movement is initiated each time a ship arrives a terminal for loading or unloading, however most of the work needs to be completed

before the actual operations begin i.e. a tactical plan, at best operational plan, needs to be put up, to be able to manage the resources in compliance with capacity demands and customer requirements efficiently.

Our interest is in previewing the requirements for handling imposed by different levels of TEU demand i.e. TEU capacity, useful for strategic decision making. The nature of handling requirements considered includes the type and number of ships, cranes, yard vehicles and other terminal facilities such as berths, ramps, and yard area. We consider the set of equipments used at the terminal level for handling a particular level of TEU capacity as a handling system. Our optimization model is intended to select between different equipments and hence different handling systems based mainly on their cost and capacity. We formulate an ILP model to consider the entire set of equipments together.

1.5Research Questions

1.5.1Main Problem

Which optimization model is suitable for developing a Decision Support System that can be used in selecting handling equipments, at different demand levels, in a port terminal involved in SSS?

The question has a broad scope; therefore we have narrowed it down to several specific issues (sub-questions) and considered a practical case.

1.5.2Sub Problems

I.Which optimization models are used in port terminals and which could be used for

developing a decision support tool?

II.Which handling equipments, used by sea ports involved in SSS, can be modelled using integer linear programming?

III.What are the requirements of SSS on sea port terminals?

The purpose of the first question is to help us understand the necessary optimization techniques in used by terminals today. The second question helps us understand the current handling situation and narrow our scope to equipments suitable for SSS and the associated problems that can be addressed using an ILP approach, while the last question helps in understanding the context within which such problems exist.

1.6Lay out of the Thesis

Section two of the thesis presents the scientific methodology, while section three consists of a literature review on SSS. Section four addresses terminal handling related problems as well as Information and Communication (ICT) technology commonly in use at port terminals, existing categorisations of handling systems, the different approaches for measuring handling performance and a description of some handling equipments commonly in used by port terminals for handling unitised cargo. In section five, is a brief introduction to the main elements of the case study(ports of Karlshamn and Klaipeda). Section six presents a description of the proposed Integer Linear Programming Optimization (ILP) model for unitised cargo terminals. Model results, analysis and validation are presented in section seven while section eight ends with a conclusion and a proposal of future work.

Chapter 2

2Research Design

This chapter explains the scientific methodology that is used in this thesis. The research design is presented.

2.1Introduction

In designing a research, one tries to formally address the primary issues of aims, purpose, intentions

and plans within constraints of location, time, money and availability of other resources. It is also

important that the chosen research design clearly makes it possible to differentiate solid traditional ideas from innovative ideas and makes clear the preferences of those who pay for the research work

and have to live with the finished results (Hakim, 1987). The main target is therefore to select strategies and methods or techniques appropriate for the question to be answered, which eventually sums up to a research work (C. Robson 2002).

2.2An Overview of the Research Method

The research has been designed following a system thinking view of the real world, with the entire system sub divided into three sub systems; Port of Karlshamn, Port of Klaipeda and the marine link between the two ports. The ILP model is developed following the operational research process. First, a literature review of current research on SSS and handling systems has been conducted. This helped understand the context and conditions under which terminal operators take decisions about handling equipments. We then develop an optimization model that can support such decisions, in the context of a case study. We proceeded by iteratively modelling the real world system (case study), developing a computer model of the system, obtaining the output, interpreting the results, and then comparing these results with the real world case and adjusting the model accordingly. This process is repeated iteratively until a point where model behaviour is seen to represent a reasonable approximation of the real world behaviour.

2.3Research Design

The research design describes the framework that has been used to address the research problem in order to establish facts which can be verified and/or used to build new ideas. In order to answer the research question, developing a computer model to select handling systems at different capacity levels, it was thought necessary to treat capacity demand as an independent variable, and the rest of the variables (e.g. handling equipments) as dependent variables for developing such a model. Such an approach is similar to a parametric research design which involves incorporating a range of several levels or values of an independent variable into an experiment so that a complete picture of it effects can be obtained (C. Robson 2002). However in developing our model, we applied the operational research methodology (L. Radon 2000) on a case study (see figure 2-1) consisting of the following steps:

?Observe the real world (case study) to identify decision problem and collect relevant information.

?Formulate an optimisation model of the real world decision problem.

?Run the model using computer system and obtain the results.

?Interpret the results and compare with real world observations (analysis & inference)

?Adjust the model accordingly until it behaviour and output can reasonably be approximated to real world observation.

?Perform a sensitivity analysis and validate the model with relevant data from different sources.

Figure 2-1 OR Process Radon (2000)

Our observation of the real world was with focus on our cases study. One way of conducting case studies is to identify the basis of the level of unit of analysis. A study where the concern remains at a single global level will be considered as a holistic case study (Yin, 1994). In our study we are interested on how a given combination of handling systems meets the handling capacity requirements of the terminal while keeping cost minimal. We consider such a case study to be holistic. The appropriateness of a holistic case study lies in the theoretical understanding of a clear, unambiguous and non-trivial set of circumstances where predicted outcomes are likely to reside (C. Robson 2002).

2.4Data and Information Collection

Data is a key issue in developing optimization models. How well the model behaves depends to a greater extent on the type of data used during model development. It is therefore necessary to get accurate data as much as possible. Most of the data used in this thesis is collected through interviews and discussion with persons involved with the issues addressed. In particular port authorities and representatives of shipping lines was our main sources of primary data. Literature reviews of scientific publications and other documentation (e.g. annual reports) enable us with secondary data. Our interest has been on empirical data.

2.4.1Secondary Data Collection

Secondary data consists of data collected from both internal and external data sources. Data collected from sources outside the case studied e.g. books, academic journals, publications and other scientific literature are considered as secondary data. One advantage with such data lies in the ease of acquisition.

2.4.2Primary Data Collection

Data a researcher sets out to collect, for the purpose of conducting a research, using a suitably designed approach can be regarded as primary data. An empirical researcher set as target to establish evidence based on empirical data collected using methods such as interviews, questionnaires, surveys

etc. Priority is usually given to primary than secondary data sources. However it is difficult to obtain data from primary sources. Besides the scarcity of primary data, where available, it is most often considered confidential thus building a barrier between the researcher and the data. Where an empirical approach does not fit well, there is every reason to consider a qualitative approach. The main approach we used to obtain primary data was through formal as well as informal interviews. Discussions (informal interviews) were seen to be valuable where the subject(s) freely express his/her views.

2.5Research Evaluation

Practically, several limitations are encountered during data collection. Some data turns out to be missing either because it is not available or it is available and treated as confidential. In other cases data that was collected for different purposes such as accounting data has to be made use of. The result of these difficulties is that some data end up in the form of estimates thereby compromising data accuracy. Evaluating the validity of data sources could minimise the risk associated with data inaccuracies.

2.5.1Evaluation

T he credibility of research wok lies in the evidences established to support the facts generated. The evidences can be built as logical arguments with references to concrete facts or as pointers to trivial issues that can be verified. Therefore in evaluating data sources one needs to ascertain these evidences by considering the following;

Validity; Lekvall and Wahlbin (1993)4 argue that the validity of a study addresses the question as to whether the researcher is actually measuring what he intends to measure. In mathematical programming, the validity of a model is the degree to which inferences drawn from the model hold for the real world (L. Radon 2000).We evaluate the internal validity of our model by running the model and comparing output results for different scenarios. While data has a direct impact on model development and behaviour, some data inaccuracies can be detected from model behaviour. Therefore model development and data collection and validation needs to be taken simultaneously. Reliability; reliability is concerned with the accuracy margin in relation to consistency. If research findings can guarantee consistency, then predictability can be made, based on the findings. To ensure that data sources are reliable it is necessary to ensure that the sources used are relevant for the question addressed in context. Further, such data can be compared with data obtained from similar cases outside the scope of the study. In the case of optimization models, consistency in model behaviour may help detect inaccuracies and hence question the reliability of some data sources. Focusing on persons directly involved in terminal handling operations as a reference we hoped the information obtained is reliable.

4

Lekvall & Whalbin, 1993, p. 211 ff.

Chapter 3

3The Structure of SSS; A Problem Perspective

In this section, we consider problems associated to SSS especially for port terminals and shippers. We find out the definition of the concept of SSS, bottlenecks and some proposed solutions with special interest on capacity, cost, and performance. To coup with SSS, terminals and shippers have to meet some basic requirements that needs corporation e.g. delivery speed.

3.1How is SSS Defined?

The concept of SSS has been widely acclaimed for it potential in combating congestion and other road traffic related problems. This means that any SSS solution has to meet some minimum requirement. Other specific qualifiers such as linking particular marine channels with given corridors do exist and varies from one region to another. As a result of these, the concept of SSS lacks a classical/coherent definition as suggested by the Canada-U.S. Memorandum of Cooperation (July 2003). The European Union (EU) Commission5has chosen to define it as follows; (1) SSS means the movement of (2) cargo and passengers by sea between ports situated in geographical Europe or between those ports and ports situated in non-European countries having a coastline on the enclosed seas bordering (3) Europe. SSS includes (4) domestic and international maritime transport including (5) feeder services along the coast to and from the islands (6) rivers and lakes. The concept of SSS also extends to maritime transport between the member States of the union and Norway and Iceland and other States on the Baltic Sea, the Black Sea and the Mediterranean. This definition suit fairly well, in line with the expectations from SSS in geographical Europe.

On the other hand the US Maritime Administration (MARAD) presents a working definition for SSS as “the commercial waterborne transportation that does not transit an ocean. It is an alternative form of commercial transportation that utilizes inland and coastal waterways to move commercial freight off already congested highways, thereby providing more efficient and safer roadways for car passengers while alleviating congestion at critical choke points. Beyond this, MARAD include the following specific requirement; a secondary effect of SSS would be reduction of air pollution and overall fuel consumption through economies of scale. Without building more highways, SSS can provide additional capacity with the National Transportation System through greater use of waterborne carriage and can enhance linkages to our North and South American trading partner (Mary R. Brooks et al; March 2006).

Worth noting, is the fact that while the definition presented by the EU uses a regional geographic criteria, the MARAD definition is based more on a functional criteria, showing how the same concept has been given different views all correct within their respective contexts. Thus, in it generalised form and for the purpose of this study we consider SSS to embrace all movement by water of passengers and/or cargo that do not include a trans-oceanic voyage and offers a feasible intermodal connection. This includes marine transport across lakes, peninsular, seas and coastal water ways shuttling between major and/or feeder ports with feasible intermodal connections. The Karlshamn-Klaipeda shipping

link can therefore be considered as a SSS link. SSS is more than just a simple marine transfer, since the intermodal connections makes the operations sophisticated with a myriad of complex dimensions (Mary R. Brooks et al; March 2006), each of which needs examination and resolution in order to render the service viable. Therefore any good attempt in characterising SSS should not only be based on the marine leg but should as well consider port terminals and particularly with regards for intermodality (integrated), which is vital in achieving a seamless flow of goods in a transport chain.

3.2What are the Current Problems Associated With SSS?

The cost incurred in investing and operating a given type of equipment is a key determinant factor considered by most terminal operators, such cost affects the price of services rendered to customers. Therefore most handling problems are closely attached to cost e.g. cost of quay crane is so high that terminals find it difficult to invest in when they are not sure of sufficient cargo traffic. This makes it necessary to consider a cost perspective in addressing some handling related problems.

3.2.1The Cost Dimension for SSS

Cost may be difficult to estimate but the main challenge lies in calculating the benefits which are not always obvious (J. T?rnquist, 2006). Although port authorities do understand SSS from an operational stand point, they often find it difficult to quantify the benefits and cost6. Not only are shippers and terminal operators unwilling to share their vital costs information, but pricing of such aspects as environmental effects, quality of service, handling performance etc may lack standards and consequently could be rather subjective. Cost estimates seem to indicate that the establishment of new regular shipping links would be considerably less costly than the construction of corresponding new land infrastructure7. S. P. Strandenes, P. B. Marlow (2000) argues that with regard to port operations, advocates of cost based pricing who point out that the basis for efficient pricing should be marginal instead of the traditional average cost pricing, take the economic point of departure. Since economies of scale exist both in providing port infrastructure and for cargo handling equipments, this pricing rule requires subsidies for ports to cover the total costs unless capital expenditure is recouped with the fixed element representing a substantial share of total cost, 80% for containers and 60% for break bulk.

In their work on the competitiveness of SSS, A. C. Paixao Casaca and P. B. Marlow identified some factors on which to develop a robust strategy for SSS, sighting cost of service (freight rates) and reliability/quality as a key factor (C. P. Casaca & P. B. Marlow; December 2005). Within the context of the U.S. industry standard cost model, the U.S. transport research board8 identifies some key cost components in a SSS projects including, cost of vessel, terminal, equipments, administration and sales, empty repositioning. These components form an operating ratio that may be used as a reference, though not unique relative to other modes of transport. Beyond this, other components such as cost of fuel, environmental effects, security, and IT systems do contribute to total cost incurred in SSS operations at the terminal level.

i.Cost of Vessel

While the capital cost of a ship is always difficult to estimate in general it can be divided into fixed cost and variable cost. Fixed costs include construction (capital), crewing, maintenance, and insurance. Whereas cost such as fuel, depreciation and amortization, can be regarded as variable cost.

6 Technical Memorandum 1, Technical findings (June 2005); Short-Sea and Coastal Shipping Options Study, Site;

http://144.202.240.28/pman/projectmanagement/Upfiles/reports/summary278.pdf last access 2006-08-06.

US Government Accountability Office (GAO, July 2005); Freight Transport: Report to Senate site;

https://www.wendangku.net/doc/5b4592985.html,/new.items/d05768.pdf last access 2008.08.07

8 I-95 Corridor Coalition (November 2005); Short Sea and Coastal Shipping Options, site;

The part of the capital varies between 20% to 70% and employment (operation) between 5% to 30%

(P. Beverskog, et a;l December 2003). Acquisition of a vessel goes beyond determining such key

pointers as vessel yard, type, quality, size, performance and terms of delivery up to key market

competitive factors and taxation policies. Most stake holders involved in SSS are of the opinion that

high-speed vessels (capable of attaining speeds of 25 to 30 knots) are necessary to support short-sea

shipping operations, but these vessels are expensive to construct and maintain, requiring a long-term

commitment by shippers who would use a short-sea service9. By the very fact that it takes about 2

years to build and deploy a vessel in water, whereas market dynamics incrementally fluctuate with

respect to infinitesimal time changes, the decision to acquire a vessel is highly strategic. As an

example, within the European market standards the cost estimate for RoRo Vs LoLo

vessel/operation carried out by A. Sj?ris on the port of G?vle indicate is shown below (Table 3.1).

RoRo LoLo

Capital Cost of Vessel (MSEK) 375 380

Port Service (MSEK/yr) 54 100

Ships (operational cost) (MSEK/yr) 143 194

Table 3-1 Cost estimates for RoRo Vs LoLo Source; third European Research Round Table Conference on

SSS, pages 86 & 138

However, such a cost structure will vary on a case by case base for different regions but investment in

vessels remains attractive from a cost perspective owing to the fact that payment is usually distributed

over a considerable time window.

ii.Cost of Terminal

Due to geographic and economic variations port terminals can vary greatly. Such variations could play

a great role in shaping the investment strategy and the type of handling systems suitable for the given

terminal. More generally some points to consider include; types of facilities and equipments to service

vessels and move cargo, difficulties of constructing berths and acquiring ramps, staging and stowage

area required, terminal access and gates, security, containers stacking patterns, environmental effects

and intermodal compatibility. B. Zigic and V. Renner 1996 highlight the cost of some of these

determinants within the Swedish economic standards: terminal access (track); 10,000 SEK/metre,

staging and stowage area (ground work); 450 to 650 SEK/square meter, equipments and facilities; 1.5

to 3.5 MSEK/ piece. These figures are indicative of the fact that in the case where land isn’t very

expensive to acquire (as in Sweden), the cost of investing in a new terminal is largely shifted to the

type of equipments and facilities required by the terminal and hence handling. The U.S. transport

research board gives an estimated operating ratio for a terminal to stand at 32.5%10, representing the

largest cost component for a SSS investment project.

iii.Cost of Equipments and Handling

As a result of diverse environmental fluctuations, cargo of different types and quality, handling

systems are the centrepieces of today’s modern terminals with an increasing need for accurate,

rational and rapid movement of material with the need to also benefit from scale economies. Among

all port activities, cargo handling is of special relevance since the cost of this service generally

represents about 80% of the costs incurred by a ship loading or unloading goods at a port (De Rus et

al. 1994; Suykens 1996). Viewed from a SSS perspective, it has been argued by some stake holders

that since shipping operators must pay dockworkers to lift cargo on or off ships, the cost of these

9I-95 Corridor Coalition (November 2005); Short Sea and Coastal Shipping Options, site;

http://144.202.240.28/pman/projectmanagement/Upfiles/reports/full343.pdf last access 2008.08.07

10 I-95 Corridor Coalition (November 2005); Short Sea and Coastal Shipping Options, site;

“lifts” will make SSS services less competitive with other modes11. It therefore follows that the decision to configure a given type of handling system for sea ports which need to derive the benefits of SSS is highly critical. Estimates from the US transport research board12 put the operating ratio for equipments in a SSS investment project to stand at 12 % including maintenance and repairs.

iv.Cost of Administration and Sales

Administrative procedures, besides imposing other bottlenecks (e.g. item declaration) at terminals on the seamless flow of goods that SSS seeks to achieve, have also been shown to constitute a significant share (estimated at 21.5% by the US Transport Research Board) of the cost structure for a SSS project. In defining the maritime policy for the EU the European Maritime Pilot Association (EMPA) included measures that encourage short sea shipping and the use of inland waterways. In particular they propose that administrative burden should at least be reduced to a level comparable to other modes of transport used for intra European traffic. However cost of administration is not an issue for ports that simply restructure to adapt to SSS, since such ports may rely on the already existing administrative body.

v.Environmental Effects

Due to their special features, sea ports are very complex systems with a wide range of environmental issues such as releases to water (e.g. waste waters, accidental releases during loading/unloading operations etc), releases to air (including gases, solid particles, energy etc) (R.M. Darbra et al; 2005). These call for a much stricter measure on ports and shipping as a whole regarding the environment, which in it natural sense has no boundaries, and thus should be approached from a corporate/global perspective. While a SSS implementation can be capable of redirecting as much as 2400 containers or 6400 truck trips from highly congested corridors, it associated high frequencies may contribute significantly to occasional discharge of pollutants (diesel, and related hydrocarbon products) from ships. Thus, to secure the environmental advantages of SSS on a regional basis, care should be taken to ensure that the diesel emission reductions gained in the urban corridors are not simply shifted to an equal or even greater amount of diesel emissions at the ports (H. D. Le-Griffin & J. E. Moore 2006). Such goals can only be attained through strong environmental pricing, and political regulations. Of 13 projects selected from 92 proposals by the EU Marco Polo program to promote government initiative for SSS, the average environmental efficiency for the 13 projects is 15 i.e. for every 1 EUR of subsidy spent 15 EUR of society external costs are saved13.These policies may vary greatly among different port and organizations, making it difficult to evaluate environmental cost parameters in it generalised sense. However for a procedure for identifying and prioritizing relevant environmental aspects in a sea port see R. M. Dabra et al (2005).

3.2.2The Capacity Dimension for SSS

The growth in world sea-born trade of containerized cargo has outstripped the growth in world trade in general since the introduction of the container in the 1950s. The sea-born volume reached an astonishing 73 million TEU in 2001, which in turn generated 230 million lift in the ports. With an average growth in container friendly cargo of 7%, it is not unlikely to expect a doubling of sea born shipment during 2001 to 2010 (P. Beverskog et al; December 2003). While this capacity growth may not pose a major challenge for maritime transport, competing market forces may impose a trade off

11 Transport Research Board (Washington, January 2004); Alternative Freight Capacity: Opportunities and Challenges. Site;

https://www.wendangku.net/doc/5b4592985.html,/onlinepubs/security/144/Richardson.ppt

12 I-95 Corridor Coalition (November 2005); Short Sea and Coastal Shipping Options, site;

http://144.202.240.28/pman/projectmanagement/Upfiles/reports/full343.pdf last access 2008.08.07

13 Transport Research Board (Washington, January 2004); Alternative Freight Capacity: Opportunities and Challenges. Site;

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