11 research outputs found

    Anรกlisis de polรญticas enfocadas a la reducciรณn de costos asociados al flujo de contenedores para carga seca, aplicado a una lรญnea naviera en Colombia

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    154 pรกginasLa toma de decisiones frente a la planeaciรณn, diseรฑo y operaciรณn de actividades para una Lรญnea Naviera especializada en carga en contenedores es una tarea compleja en cuanto a la gestiรณn sobre los flujos de contenedores se refiere. Algunos modelos han sido desarrollados con el fin de mejorar el desempeรฑo en el manejo de los flujos de contenedores, sin embargo es preciso diseรฑar modelos especรญficos para trabajar problemรกticas puntuales. Este proyecto comprende la descripciรณn del problema asociado al manejo de contenedores por parte de una Lรญnea Naviera en Colombia, la construcciรณn y validaciรณn de un modelo empleando dinรกmica de sistemas, y la evaluaciรณn del impacto de polรญticas frente a distintos escenarios con el fin de analizar el posible desempeรฑo de la gestiรณn de costos y nivel de servicio

    Tank container operatorsโ€™ profit maximization through dynamic operations planning integrated with the quotation-booking process under multiple uncertainties

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    Tank Container Operators (TCOs) are striving to maximize profit through the integration of their global Tank Container (TC) operations with the job quotation-booking process. However, TCOs face a set of unique challenges not faced by general shipping container operators, including the process uncertainties arising from TC cleaning and the use of Freight Forwarders (FFs). In this paper, a simulation-based two-stage optimization model is developed to address these challenges. The first stage focuses on tactical decisions of setting inventory levels and control policy for empty container repositioning. The second stage integrates the dynamic job acceptance/rejection decisions in the quotation-booking processes with container operations decisions in the planning and execution processes, such as job fulfilment, container leasing terms, choice of FFs considering cost and reliability, and empty tank container repositioning. The solution procedure is based on the simulation model combined with heuristic algorithms including an adjusted Genetic Algorithm, mathematical programming, and heuristic rules. Numerical examples based on a real case study are provided to illustrate the effectiveness of the model.Tank Container Operators (TCOs) are striving to maximize profit through the integration of their global Tank Container (TC) operations with the job quotation-booking process. However, TCOs face a set of unique challenges not faced by general shipping container operators, including the process uncertainties arising from TC cleaning and the use of Freight Forwarders (FFs). In this paper, a simulation-based two-stage optimization model is developed to address these challenges. The first stage focuses on tactical decisions of setting inventory levels and control policy for empty container repositioning. The second stage integrates the dynamic job acceptance/rejection decisions in the quotation-booking processes with container operations decisions in the planning and execution processes, such as job fulfillment, container leasing terms, choice of FFs considering cost and reliability, and empty tank container repositioning. The solution procedure is based on the simulation model combined with heuristic algorithms including an adjusted Genetic Algorithm, mathematical programming, and heuristic rules. Numerical examples based on a real case study are provided to illustrate the effectiveness of the model

    ํ•ด์šด๋ฌผ๋ฅ˜์—์„œ์˜ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ ํšจ๊ณผ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2022.2. ๋ฌธ์ผ๊ฒฝ.์ปจํ…Œ์ด๋„ˆ ํ™” ์ดํ›„๋กœ ํ•ด์ƒ ๋ฌผ๋ฅ˜๋Š” ํญ๋ฐœ์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜์˜€๊ณ  ์„ธ๊ณ„ํ™”์™€ ์‚ฐ์—… ๋ฐœ์ „์„ ์„ ๋„ํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ๋ฌด์—ญ๋Ÿ‰์˜ ์ฆ๊ฐ€์™€ ๋น„๋ก€ํ•˜์—ฌ ์ˆ˜์ถœ์ž… ๋ถˆ๊ท ํ˜•์œผ๋กœ ์ธํ•œ ์ปจํ…Œ์ด๋„ˆ์˜ ๋ถˆ๊ท ํ˜• ๋ฌธ์ œ๋„ ์‹ฌํ™”๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ์ž๋“ค์˜ ๋…ธ๋ ฅ์ด ์žˆ์—ˆ๊ณ , ๊ทธ ์ค‘ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๋ผ๋Š” ์ƒˆ๋กœ์šด ๊ฐœ๋…์˜ ์ปจํ…Œ์ด๋„ˆ๊ฐ€ ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ์•„์ง ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๋Š” ์ƒ์šฉํ™” ์ดˆ๊ธฐ ๋‹จ๊ณ„์ด๋ฉฐ, ์ด๋ฅผ ํ™œ์šฉํ•œ ์—ฌ๋Ÿฌ ํšจ๊ณผ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ•œ ์‹ค์ •์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๊ฐ€ ๋„์ž…๋˜์—ˆ์„ ๋•Œ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์˜ํ–ฅ๊ณผ ๊ทธ ํšจ๊ณผ์— ๋Œ€ํ•ด ๋‹ค๋ฃจ์—ˆ๋‹ค. ๋จผ์ € ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๊ฐ€ ํฌ๋ ˆ์ธ ํ™œ๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ณ , ์ „์—ญ์  ๊ด€์ ์œผ๋กœ ํฌ๋ ˆ์ธ ํ™œ๋™์„ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ ์œก์ƒ์—์„œ์˜ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ ์ ์šฉ์ด ํ•ด์ƒ๊ณผ๋Š” ๋‹ค๋ฅด๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•˜์—ฌ ๊ทธ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ 2008 ๊ธˆ์œต์œ„๊ธฐ์™€ COVID-19 ์ดํ›„์— ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋Š” ํ•ด์šด๋ฌผ๋ฅ˜์˜ ๊ฐ์ข… ๋ณ€๋™ํ•˜๋Š” ์ƒํ™ฉ ํ•˜์—์„œ์˜ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ ํšจ๊ณผ์— ๋Œ€ํ•ด ์ƒˆ๋กœ์šด ํ†ต์ฐฐ์„ ์ œ๊ณตํ•˜์˜€๋‹ค. 1์žฅ์—์„œ๋Š” ๊ฐ„๋‹จํ•˜๊ฒŒ ์ปจํ…Œ์ด๋„ˆํ™”์™€ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ์— ๋Œ€ํ•ด ์„ค๋ช…ํ•˜๊ณ  ๋ฌธ์ œ๋ฅผ ์ฃผ๋ชฉํ•˜๊ฒŒ ๋œ ์ด์œ ์™€ ๊ทธ ์„ฑ๊ณผ๋ฅผ ์„œ์ˆ ํ•˜์˜€๋‹ค. 2์žฅ์—์„œ๋Š” ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๊ฐ€ ๋„์ž…๋จ์— ๋”ฐ๋ผ ์ƒ๊ธธ ์ˆ˜ ์žˆ๋Š” โ€˜์ƒ๋‹จ ์ ์žฌ ๊ทœ์น™โ€™์ด ์ ์šฉ๋˜์—ˆ์„ ๋•Œ์˜ ํฌ๋ ˆ์ธ ํ™œ๋™์˜ ๋ณ€ํ™”๋ฅผ ์‚ดํŽด๋ณด๊ณ  ์ „์—ญ์  ์ตœ์ ํ™”๊ฐ€ ์ง€์—ญ์  ์ตœ์ ํ™”๋ณด๋‹ค ํšจ๊ณผ์ ์ž„์„ ๋ณด์˜€๋‹ค. ๋”๋ถˆ์–ด ์ „์—ญ์  ์ตœ์ ํ™”๋ฅผ ๋„์ž…ํ•˜์˜€์„ ๋•Œ ์ง๋ฉดํ•  ์ˆ˜ ์žˆ๋Š” ๋น„์šฉ ๋ถ„๋ฐฐ ๋ฌธ์ œ์— ๋Œ€ํ•ด์„œ๋„ ์กฐ๋งํ•˜์—ฌ ๊ทธ ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•˜์˜€๋‹ค. 3์žฅ์—์„œ๋Š” ์œก์ƒ์—์„œ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๊ฐ€ ์ˆ˜์†ก๊ณต๊ฐ„์„ ์ค„์—ฌ์ฃผ๋Š” ์žฅ์  ์™ธ์— ๊ฒฝ๋กœ๋ฅผ ๋ฐ”๊พธ๋Š” ํšจ๊ณผ๊ฐ€ ์กด์žฌํ•จ์„ ๋ณด์ด๊ณ , ๋‹ค์–‘ํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค์™€ ์ •์ฑ…์— ๋”ฐ๋ผ ๊ทธ ํšจ๊ณผ๊ฐ€ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”ํ•˜๋Š”์ง€์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. 4์žฅ์—์„œ๋Š” ์ฆ๊ฐ€ํ•˜๋Š” ๋‹ค์–‘ํ•œ ๋ณ€๋™์ƒํ™ฉ ๊ฐ๊ฐ์— ๋Œ€ํ•ด ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ์˜ ํšจ๊ณผ์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ† ๋Œ€๋กœ ๊ฐ ์ƒํ™ฉ์— ๋งž๋Š” ์ตœ์  ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ ๊ฐœ์ˆ˜๋ฅผ ๋„์ถœํ•˜๊ณ  ์ž„๋Œ€ ์ •์ฑ…์„ ํ†ตํ•ด ๋Œ€์‘ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ํ†ต์ฐฐ์„ ๋„์ถœํ•˜์˜€๋‹ค. 5์žฅ์—์„œ๋Š” ๋ณธ ๋…ผ๋ฌธ์˜ ๊ฒฐ๋ก ๊ณผ ํ–ฅํ›„ ์—ฐ๊ตฌ ๋ฐฉ์•ˆ์— ๋Œ€ํ•ด ์„œ์ˆ ํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜๋Š” ๋ฌธ์ œ์™€ ๊ทธ ํ•ด๊ฒฐ ๋ฐฉ๋ฒ•์€ ํ•™์ˆ ์  ๋ฐ ์‚ฐ์—…์ ์œผ๋กœ ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค. ํ•™๊ณ„์—๋Š” ์‹ค์ œ ์กด์žฌํ•˜๋Š” ํ˜„์žฅ์˜ ๋ฌธ์ œ๋“ค์„ ์ œ์‹œํ•˜๊ณ  ๋ฌธ์ œ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•๋“ค์„ ์ œ์•ˆํ•œ๋‹ค. ์‚ฐ์—…๊ณ„์—๋Š” ์‹ ๊ธฐ์ˆ ์ธ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ์˜ ๋„์ž…์— ๋”ฐ๋ผ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋ฌธ์ œ์— ๋Œ€ํ•ด ์ •๋Ÿ‰ํ™” ๋ฐ ๋ชจํ˜•ํ™”๋ฅผ ํ†ตํ•œ ํ•ด๊ฒฐ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์„ ํ†ตํ•ด ์‚ฐ์—…์˜ ๋ฐœ์ „๊ณผ ํ•™๋ฌธ์˜ ๋ฐœ์ „์ด ํ•จ๊ป˜ ์ด๋ฃจ์–ด์งˆ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.After containerization, maritime logistics experienced the substantial growth of trade volumes and led to globalization and industrial development. However, in proportion to the increase in the volume, the degree of container imbalance also intensified due to the disparity between importing and exporting sizes at ports in different continents. A group of researchers is digging into resolving this ongoing challenge, and a new concept of a container, called a foldable container, has been proposed. Nevertheless, foldable containers are still in the early stage of commercialization, and research on the various effects of using foldable containers seems insufficient yet. This dissertation considers the possible effects of the introduction of foldable containers. First, we analyze the effect of foldable containers on crane operation and reduce shifts from a global perspective. Second, the effect of using foldable containers in hinterland areas was analyzed by noting that the application of foldable containers on land was different from that of the sea. Finally, we provided new insights into the foldable container under plausible dynamic situations in the shipping industry during the COVID-19 and logistics that have increased since the 2008 financial crisis. A brief explanation of containerization and foldable containers is introduced in Chapter 1, along with the dissertation's motivations, contributions, and outlines. Chapter 2 examines changes in crane operation when the 'top stowing rule' that can be treated with foldable containers is applied and shows that global optimization is more effective than local optimization. In addition, we suggested the cost-sharing method to deal with fairness issues for additional costs between ports when the global optimization method is fully introduced. Chapter 3 shows that foldable containers in the hinterland have the effect of changing routes in addition to reducing transportation space and analyzes how the results change according to various scenarios and policies. Chapter 4 analyzes the effectiveness of foldable containers for different dynamic situations. Moreover, the managerial insight was derived that the optimal number of foldable containers suitable for each situation can be obtained and responded to leasing policies. Chapter 5 describes the conclusions of this dissertation and discusses future research. The problem definition and solution methods proposed in this dissertation can be seen as meaningful in both academic and industrial aspects. For academia, we presented real-world problems in the field and suggested ways to solve problems effectively. For industry, we offered solutions through quantification and modeling for real problems related to foldable containers. We expect that industrial development and academic achievement can be achieved together through this dissertation.Chapter 1 Introduction 1 1.1 Containerization and foldable container 1 1.2 Research motivations and contributions 3 1.3 Outline of the dissertation 6 Chapter 2 Efficient stowage plan with loading and unloading operations for shipping liners using foldable containers and shift cost-sharing 7 2.1 Introduction 7 2.2 Literature review 10 2.3 Problem definition 15 2.4 Mathematical model 19 2.4.1 Mixed-integer programming model 19 2.4.2 Cost-sharing 24 2.5 Computational experiment and analysis 26 2.6 Conclusions 34 Chapter 3 Effects of using foldable containers in hinterland areas 36 3.1 Introduction 36 3.2 Single depot repositioning problem 39 3.2.1 Problem description 40 3.2.2 Mathematical formulation of the single depot repositioning problem 42 3.2.3 Effects of foldable containers 45 3.3 Multi-depot repositioning problem 51 3.4 Computational experiments 56 3.4.1 Experimental design for the SDRP 57 3.4.2 Experimental results for the SDRP 58 3.4.3 Major and minor effects with the single depot repositioning problem 60 3.5 Conclusions 65 Chapter 4 Effect of foldable containers in dynamic situation 66 4.1 Introduction 66 4.2 Problem description 70 4.3 Mathematical model 73 4.4 Computational experiments 77 4.4.1 Overview 77 4.4.2 Experiment results 79 4.5 Conclusions 88 Chapter 5 Conclusion and future research 90 Bibliography 94 ๊ตญ๋ฌธ์ดˆ๋ก 99๋ฐ•

    Economic Analysis of Marine Transportation using Foldable Container

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    Today, businesses are faced with problems such as rapidly changing technology, infinite competition and uncertainty of economic growth. To solve these problems, many companies are trying to strengthen their competitiveness by sharing resources and profits, spreading the risk of large-scale investment through collaboration. In particular, logistics companies are sensitive to changes in the economy. The Korean shipping industry has been in a state of crisis since the 2008 global financial crisis, with the ongoing recession and prolonged oversupply and shipping depression. Korea's international freight transport accounts for more than 90% of the import and export volume. These maritime transports are linked to the land and the cargo is transported in the form of multimodal transport. Containers carry these cargoes and are transported to their destination through various logistics bases in the form of composite transportation using transportation means such as ships, automobiles, and railways. Globally, 30 container shipping companies are paying about $ 20 billion a year for repositioning of empty containers. This is due to the imbalance in world trade and mainly to relocate public containers to Asia. The introduction of folding containers in maritime transport can be one of the solutions to this problem. Because Jeju Island is geographically island, Korea must use sea or air transportation in economic activities with the inland, and logistics cost is relatively high. Therefore, in order to improve the competitiveness of the region or to achieve the goal of implementing Jeju as an eco-friendly island, it is necessary to utilize the logistics related resources as much as possible to reduce costs and minimize the environmental pollution. In addition to its environmentally friendly features, folding containers are also recognized as a means of reducing total logistics costs such as marine transportation and land transportation. This study proposes the use of folding containers for marine transportation between Jeju and Mokpo, and analyzes the economics through various scenarios when folding containers are operated. Based on the conclusions, Various suggestions are also presented.ํ‘œ๋ชฉ์ฐจ โ…ด ๊ทธ๋ฆผ๋ชฉ์ฐจ โ…ถ ์˜๋ฌธ์ดˆ๋ก(Abstract) โ…ธ 1. ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 1.1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.1.2 ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 2 1.2 ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๋ฐ ๋ฒ”์œ„ 5 1.2.1 ์—ฐ๊ตฌ์˜ ๋ชฉ์  5 1.2.2 ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 6 1.3 ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• ๋ฐ ๊ตฌ์„ฑ 7 1.4 ์„ ํ–‰์—ฐ๊ตฌ์™€์˜ ์ฐจ๋ณ„์„ฑ 9 2. ์ด๋ก ์  ๊ณ ์ฐฐ 10 2.1 ์ œ์ฃผ-๋ชฉํฌ ํ•ญ๋กœ์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 10 2.2 ๊ณต ์ปจํ…Œ์ด๋„ˆ ์žฌ๋ฐฐ์น˜์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 12 2.3 ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 17 2.4 ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ์˜ ๊ฐœ๋ฐœํ˜„ํ™ฉ 20 2.5 Container Pool System์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 25 2.5.1 Container Pool System์˜ ์ •์˜ 25 2.5.2 Container Pool System์˜ ํŠน์ง•๊ณผ ์„ ํ–‰์—ฐ๊ตฌ 25 3. ์ œ์ฃผ-๋ชฉํฌ๊ฐ„์˜ ํ•ด์ƒ์šด์†ก์˜ ํ˜„ํ™ฉ๊ณผ ๋ฌธ์ œ์  28 3.1 ์ œ์ฃผ-๋ชฉํฌ๊ฐ„์˜ ํ•ด์ƒ์šด์†ก ํŠน์„ฑ ๋ฐ ํ˜„ํ™ฉ๊ณผ ๋ฌธ์ œ์  28 3.1.1 ์ œ์ฃผ๋„์˜ ์ง€๋ฆฌ์  ํŠน์„ฑ 28 3.1.2 ์ œ์ฃผ-๋ชฉํฌ๊ฐ„์˜ ํ•ด์ƒ์šด์†ก์˜ ํ˜„ํ™ฉ 30 3.1.3 ์ œ์ฃผ-๋ชฉํฌ๊ฐ„์˜ ํ•ด์ƒ๋ฌผ๋ฅ˜์˜ ๋ฌธ์ œ์  34 3.2 ์ œ์ฃผ-๋ชฉํฌ๊ฐ„์˜ ํ•ด์ƒ์šด์†ก์˜ ํ˜•ํƒœ์™€ ์ปจํ…Œ์ด๋„ˆ ์ด์šฉ์‹คํƒœ 36 3.2.1 ์ œ์ฃผ-๋ชฉํฌ๊ฐ„์˜ ํ•ด์ƒ์šด์†ก์˜ ํ˜•ํƒœ 36 3.2.2 ์ œ์ฃผ-๋ชฉํฌ๊ฐ„์˜ ์ปจํ…Œ์ด๋„ˆ ์ด์šฉ ์‹คํƒœ 38 3.2.3 ์ œ์ฃผ-๋ชฉํฌ๊ฐ„์˜ ์ปจํ…Œ์ด๋„ˆ ์šด์˜๋ฐฉ์‹ 42 4. ์‹œ๋‚˜๋ฆฌ์˜ค ์ ์šฉ๊ณผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 43 4.1 ์‹œ๋‚˜๋ฆฌ์˜ค ํ”Œ๋ž˜๋‹ 43 4.2 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 46 4.2.1 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ชจ๋ธ 46 4.2.2 ์ œ์ฃผ-๋ชฉํฌํ•ญ๋กœ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 48 4.3 ์ปจํ…Œ์ด๋„ˆ ์šด์˜๋น„์šฉ ๋ถ„์„ 50 4.3.1 One Cycle(Shipper to Consignee)๋น„์šฉ ๋ถ„์„ 50 4.3.2 One Cycle(Port to Port)๋น„์šฉ ๋ถ„์„ 52 4.3.3 One-way Empty Container repositioning๋น„์šฉ ๋ถ„์„ 53 5. ๊ฒฝ์ œ์„ฑ ๋น„๊ต๋ถ„์„ 55 5.1 ์šด์˜๊ตฌ๊ฐ„์— ๋”ฐ๋ฅธ ๋น„๊ต๋ถ„์„ 55 5.2 ์ œ์ฃผ-๋ชฉํฌํ•ญ๋กœ ๋ฌผ๋™๋Ÿ‰์— ๋”ฐ๋ฅธ ๋น„๊ต๋ถ„์„ 56 5.3 ์ œ์ฃผ-๋ชฉํฌํ•ญ๋กœ ๊ณต ์ปจํ…Œ์ด๋„ˆ ์žฌ๋ฐฐ์น˜ ๋น„์šฉ ๋น„๊ต ๋ถ„์„ 57 5.4 ๋ฏผ๊ฐ๋„ ๋ถ„์„ 60 6. ๊ฒฐ๋ก  65 6.1 ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ ๋ฐ ์‹œ์‚ฌ์  65 6.2 ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ ๋ฐฉํ–ฅ 67 ๊ฐ์‚ฌ์˜ ๊ธ€ 68 ์ฐธ๊ณ ๋ฌธํ—Œ 69 ๋ถ€๋ก 2016๋…„๋„ ํ•ญ๋งŒํ•˜์—ญ์š”๊ธˆํ‘œ 71Maste

    ๊ณต์ปจํ…Œ์ด๋„ˆ๊ด€๋ฆฌ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•œ ํšจ์œจ์ ์ธ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2021. 2. ๋ฌธ์ผ๊ฒฝ.Due to a remarkable surge in global trade volumes led by maritime transportation, shipping companies should make a great effort in managing their container flows especially in case of carrier-owned containers. To do so, they comprehensively implement empty container management strategies and accelerate the flows in a cost- and time-efficient manner to minimize total relevant costs while serving the maximal level of customers demands. However, many critical issues in container flows universally exist due to high uncertainty in reality and hinder the establishment of an efficient container supply chain. In this dissertation, we fully discuss such issues and provide mathematical models along with specific solution procedures. Three types of container supply chain are presented in the following: (i) a two-way four-echelon container supply chain; (ii) a laden and empty container supply chain under decentralized and centralized policies; (iii) a reliable container supply chain under disruption. These models explicitly deal with high risks embedded in a container supply chain and their computational experiments offer underlying managerial insights for the management in shipping companies. For (i), we study empty container management strategy in a two-way four-echelon container supply chain for bilateral trade between two countries. The strategy reduces high maritime transportation costs and long delivery times due to transshipment. The impact of direct shipping is investigated to determine the number of empty containers to be repositioned among selected ports, number of leased containers, and route selection to satisfy the demands for empty and laden containers for exporters and importers in two regions. A hybrid solution procedure based on accelerated particle swarm optimization and heuristic is presented, and corresponding results are compared. For (ii), we introduce the laden and empty container supply chain model based on three scenarios that differ with regard to tardiness in the return of empty containers and the decision process for the imposition of fees with the goal of determining optimal devanning times. The effectiveness of each type of policy - centralized versus decentralized - is determined through computational experiments that produce key performance measures including the on-time return ratio. Useful managerial insights on the implementation of these polices are derived from the results of sensitivity analyses and comparative studies. For (iii), we develop a reliability model based on container network flow while also taking into account expected transportation costs, including street-turn and empty container repositioning costs, in case of arc- and node-failures. Sensitivity analyses were conducted to analyze the impact of disruption on container supply chain networks, and a benchmark model was used to determine disruption costs. More importantly, some managerial insights on how to establish and maintain a reliable container network flow are also provided.ํ•ด์ƒ ์ˆ˜์†ก์ด ์ฃผ๋„ํ•จ์œผ๋กœ์จ ์ „ ์„ธ๊ณ„ ๋ฌด์—ญ๋Ÿ‰์ด ๊ธ‰์ฆํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํšŒ์‚ฌ ์†Œ์œ  ์ปจํ…Œ์ด๋„ˆ๋Š” ์ปจํ…Œ์ด๋„ˆ ํ๋ฆ„์„ ๊ด€๋ฆฌํ•˜๋Š” ๋ฐ ๋งŽ์€ ๋…ธ๋ ฅ์„ ๊ธฐ์šธ์—ฌ์•ผ ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ณต ์ปจํ…Œ์ด๋„ˆ ๊ด€๋ฆฌ ์ „๋žต์„ ํฌ๊ด„์ ์œผ๋กœ ๊ตฌํ˜„ํ•˜๊ณ  ํšจ์œจ์ ์ธ ์ˆ˜์†ก ๋น„์šฉ ๋ฐ ์‹œ๊ฐ„ ์ ˆ๊ฐ ๋ฐฉ์‹์œผ๋กœ ์ปจํ…Œ์ด๋„ˆ ํ๋ฆ„์„ ์›ํ™œํžˆ ํ•˜์—ฌ ๊ด€๋ จ ์ด๋น„์šฉ์„ ์ตœ์†Œํ™”ํ•˜๋Š” ๋™์‹œ์— ๊ณ ๊ฐ์˜ ์ˆ˜์š”๋ฅผ ์ตœ๋Œ€ํ•œ ์ถฉ์กฑํ•˜๊ฒŒ ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ˜„์‹ค์—์„œ๋Š” ๋†’์€ ๋ถˆํ™•์‹ค์„ฑ ๋•Œ๋ฌธ์— ์ปจํ…Œ์ด๋„ˆ ํ๋ฆ„์— ๋Œ€ํ•œ ๋งŽ์€ ์ฃผ์š”ํ•œ ์ด์Šˆ๊ฐ€ ๋ณดํŽธ์ ์œผ๋กœ ์กด์žฌํ•˜๊ณ  ํšจ์œจ์ ์ธ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง ๊ตฌ์ถ•์„ ๋ฐฉํ•ดํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ์ด์Šˆ์— ๋Œ€ํ•ด ์ „๋ฐ˜์ ์œผ๋กœ ๋…ผ์˜ํ•˜๊ณ  ์ ์ ˆํ•œ ํ•ด๋ฒ•๊ณผ ํ•จ๊ป˜ ์ˆ˜๋ฆฌ ๋ชจํ˜•์„ ์ œ๊ณตํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์„ธ ๊ฐ€์ง€ ์œ ํ˜•์˜ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง์„ ๋‹ค๋ฃฌ๋‹ค. ๋จผ์ € (i) ์–‘๋ฐฉํ–ฅ ๋„ค ๋‹จ๊ณ„ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง, (ii) ๋ถ„๊ถŒํ™” ๋ฐ ์ค‘์•™ ์ง‘์ค‘ํ™” ์ •์ฑ…์— ๋”ฐ๋ฅธ ์ โˆ™๊ณต ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง; ๊ทธ๋ฆฌ๊ณ  (iii) disruption ์ƒํ™ฉ ์†์—์„œ ์‹ ๋ขฐ์„ฑ์„ ๊ณ ๋ คํ•˜๋Š” ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์‹œํ•œ ์„ธ ๊ฐ€์ง€ ๋ชจํ˜•์€ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง์— ๋‚ด์žฌ ๋œ ๋†’์€ ์œ„ํ—˜์„ ์ง์ ‘ ๋‹ค๋ฃจ๋ฉฐ ๊ณ„์‚ฐ ์‹คํ—˜์€ ํ•ด์šด ํšŒ์‚ฌ์˜ ๊ฒฝ์˜์ง„์ด๋‚˜ ๊ด€๊ณ„์ž๋ฅผ ์œ„ํ•ด ์ฃผ์š”ํ•œ ๊ด€๋ฆฌ ์ธ์‚ฌ์ดํŠธ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. (i)์˜ ๊ฒฝ์šฐ, ๋‘ ์ง€์—ญ ๊ฐ„ ์–‘์ž ๋ฌด์—ญ์„ ์œ„ํ•œ ์–‘๋ฐฉํ–ฅ ๋„ค ๋‹จ๊ณ„ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง์—์„œ ๊ณต ์ปจํ…Œ์ด๋„ˆ ๊ด€๋ฆฌ ์ „๋žต์„ ์—ฐ๊ตฌํ•œ๋‹ค. ์ด ์ „๋žต์€ ํ™˜์ ์œผ๋กœ ์ธํ•œ ๋†’์€ ํ•ด์ƒ ์šด์†ก ๋น„์šฉ๊ณผ ๊ธด ๋ฐฐ์†ก ์‹œ๊ฐ„์„ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ์งํ•ญ ์ˆ˜์†ก์˜ ์˜ํ–ฅ์„ ์กฐ์‚ฌํ•˜์—ฌ ์„ ํƒ๋œ ํ•ญ๊ตฌ ์ค‘ ์žฌ๋ฐฐ์น˜ ํ•  ๊ณต ์ปจํ…Œ์ด๋„ˆ ์ˆ˜, ์ž„๋Œ€ ์ปจํ…Œ์ด๋„ˆ ์ˆ˜, ๋‘ ์ง€์—ญ์˜ ์ˆ˜์ถœ์—…์ž์™€ ์ˆ˜์ž…์—…์ž์˜ ์ โˆ™๊ณต ์ปจํ…Œ์ด๋„ˆ ๋Œ€ํ•œ ์ˆ˜์š”๋ฅผ ๋งŒ์กฑํ•˜๊ธฐ ์œ„ํ•œ ๊ฒฝ๋กœ ์„ ํƒ์„ ๊ฒฐ์ •ํ•˜๊ฒŒ ๋œ๋‹ค. APSO ๋ฐ ํœด๋ฆฌ์Šคํ‹ฑ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ํ•ด๋ฒ•์„ ์ œ์‹œํ•˜๋ฉฐ ๋น„๊ต ์‹คํ—˜์„ ํ•˜์˜€๋‹ค. (ii)์˜ ๊ฒฝ์šฐ ์ตœ์  devanning time ๊ฒฐ์ •์„ ๋ชฉํ‘œ๋กœ ๊ณต ์ปจํ…Œ์ด๋„ˆ์˜ ๋ฐ˜ํ™˜ ์ง€์—ฐ๊ณผ ํ•ด๋‹น ์ˆ˜์ˆ˜๋ฃŒ ๋ถ€๊ณผ ๊ฒฐ์ • ํ”„๋กœ์„ธ์Šค์™€ ๊ด€๋ จํ•˜์—ฌ ์„œ๋กœ ๋‹ค๋ฅธ ์„ธ ๊ฐ€์ง€ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ โˆ™๊ณต ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง ๋ชจํ˜•์„ ์ œ์‹œํ•œ๋‹ค. ๊ฐ ์œ ํ˜•์˜ ์ •์ฑ…์ (๋ถ„๊ถŒํ™” ๋ฐ ์ค‘์•™ ์ง‘์ค‘ํ™”) ํšจ๊ณผ๋Š” ์ •์‹œ ๋ฐ˜ํ™˜์œจ์„ ํฌํ•จํ•œ ์ฃผ์š” ์„ฑ๋Šฅ ์ธก์ •์„ ๊ณ ๋ คํ•˜๋Š” ๊ณ„์‚ฐ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒฐ์ •๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ •์ฑ… ์‹คํ–‰์— ๋Œ€ํ•œ ์œ ์šฉํ•œ ๊ด€๋ฆฌ ์ธ์‚ฌ์ดํŠธ๋Š” ๋ฏผ๊ฐ๋„ ๋ถ„์„ ๋ฐ ๋น„๊ต ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ์—์„œ ๋„์ถœํ•œ๋‹ค. (iii)์˜ ๊ฒฝ์šฐ, ๋ณธ ๋…ผ๋ฌธ์€ ์ปจํ…Œ์ด๋„ˆ ๋„คํŠธ์›Œํฌ ํ๋ฆ„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์‹ ๋ขฐ์„ฑ ๋ชจํ˜•์„ ๊ฐœ๋ฐœํ•˜๋Š” ๋™์‹œ์— ์•„ํฌ ๋ฐ ๋…ธ๋“œ failure๊ฐ€ ์žˆ์„ ๋•Œ street-turn ๋ฐ ๊ณต ์ปจํ…Œ์ด๋„ˆ ์žฌ๋ฐฐ์น˜ ๋น„์šฉ์„ ํฌํ•จํ•œ ๊ธฐ๋Œ€ ์ด ๋น„์šฉ์„ ๊ตฌํ•œ๋‹ค. ์ค‘๋‹จ์ด ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง ๋„คํŠธ์›Œํฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ๋ฏผ๊ฐ๋„ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ–ˆ์œผ๋ฉฐ disruption ๋น„์šฉ์„ ๊ฒฐ์ •ํ•˜๊ธฐ ์œ„ํ•ด ๋ฒค์น˜๋งˆํฌ ๋ชจํ˜•์„ ํ™œ์šฉํ•œ๋‹ค. ๋”๋ถˆ์–ด ์‹ ๋ขฐ์„ฑ์„ ๊ณ ๋ คํ•œ ์ปจํ…Œ์ด๋„ˆ ๋„คํŠธ์›Œํฌ ํ๋ฆ„์„ ๊ตฌ์ถ•ํ•˜๊ณ  ์‹ ๋ขฐ์„ฑ์„ ์œ ์ง€ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ๊ด€๋ฆฌ์  ์ธ์‚ฌ์ดํŠธ๋„ ์ œ๊ณตํ•œ๋‹ค.Abstract i Contents ii List of Tables vi List of Figures viii 1. Introduction 1 1.1 Empty Container Repositioning Problem 1 1.2 Reliability Problem 3 1.3 Research Motivation and Contributions 4 1.4 Outline of the Dissertation 7 2. Two-Way Four-Echelon Container Supply Chain 8 2.1 Problem Description and Literature Review 8 2.2 Mathematical Model for the TFESC 15 2.2.1 Overview and Assumptions 15 2.2.2 Notation and Formulation 19 2.3 Solution Procedure for the TFESC 25 2.3.1 Pseudo-Function-based Optimization Problem 25 2.3.2 Objective Function Evaluation 28 2.3.3 Heuristics for Reducing the Number of Leased Containers 32 2.3.4 Accelerated Particle Swarm Optimization 34 2.4 Computational Experiments 37 2.4.1 Heuristic Performances 39 2.4.2 Senstivity Analysis of Varying Periods 42 2.4.3 Senstivity Analysis of Varying Number of Echelons 45 2.5 Summary 48 3. Laden and Empty Container Supply Chain under Decentralized and Centralized Policies 50 3.1 Problem Description and Literature Review 50 3.2 Scenario-based Model for the LESC-DC 57 3.3 Model Development for the LESC-DC 61 3.3.1 Centralized Policy 65 3.3.2 Decentralized Policies (Policies I and II) 67 3.4 Computational Experiments 70 3.4.1 Numerical Exmpale 70 3.4.2 Sensitivity Analysis of Varying Degree of Risk in Container Return 72 3.4.3 Sensitivity Analysis of Increasing L_0 74 3.4.4 Sensitivity Analysis of Increasing t_r 76 3.4.5 Sensitivity Analysis of Decreasing es and Increasing e_f 77 3.4.6 Sensitivity Analysis of Discounting ใ€–pnใ€—_{f1} and ใ€–pnใ€—_{f2} 78 3.4.7 Sensitivity Analysis of Different Container Fleet Sizes 79 3.5 Managerial Insights 81 3.6 Summary 83 4. Reliable Container Supply Chain under Disruption 84 4.1 Problem Description and Literature Review 84 4.2 Mathematical Model for the RCNF 90 4.3 Reliability Model under Disruption 95 4.3.1 Designing the Patterns of q and s 95 4.3.2 Objective Function for the RCNF Model 98 4.4 Computational Experiments 103 4.4.1 Sensitivity Analysis of Expected Failure Costs 106 4.4.2 Sensitivity Analysis of Different Network Structures 109 4.4.3 Sensitivity Analysis of Demand-Supply Variation 112 4.4.4 Managerial Insights 115 4.5 Summary 116 5. Conclusions and Future Research 117 Appendices 120 A Proof of Proposition 3.1 121 B Proof of Proposition 3.2 124 C Proof of Proposition 3.3 126 D Sensitivity Analyses for Results 129 E Data for Sensitivity Analyses 142 Bibliography 146 ๊ตญ๋ฌธ์ดˆ๋ก 157 ๊ฐ์‚ฌ์˜ ๊ธ€ 160Docto

    Maritime Empty Container Repositioning with Inventory-based Control

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    Ph.DDOCTOR OF PHILOSOPH

    Operational model for empty container repositioning

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    Ph.DDOCTOR OF PHILOSOPH

    Models and algorithms for the empty container repositioning and its integration with routing problems

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    The introduction of containers has fostered intermodal freight transportation. A definition of intermodality was provided by the European Commission as โ€œa characteristic of a transport system whereby at least two different modes are used in an integrated manner in order to complete a door-to-door transport sequenceโ€. The intermodal container transportation leads to several benefits, such as higher productivity during handling phases and advantages in terms of security, losses and damages. However, the distribution of containers comes with a drawback: due to directional imbalances in freight flows, some areas tend to accumulate unnecessary empty containers, while others face container shortages. Several planning models were developed for carriers in order to manage both loaded and empty containers profitably. However, they were built to operate under normal circumstances, neglecting the fact that networks are increasingly affected by both uncertainty and vulnerability, which may result in disruptions. The thesis aims to survey whether the impact of uncertainty can be mitigated by a stochastic programming approach, in which disruptions and normal operations are both foreseen as possible futures or scenarios. This approach is carried out by a multi-scenario optimization model in which scenarios are linked by non-anticipativity conditions. The empty container repositioning becomes even more challenging and difficult when integrated with routing problems. In fact, carriers often face problems in which they must determine simultaneously how many empty containers are carried by a fleet of vehicles and which routes must be followed by these vehicles. These problems typically arise in inland networks, in which one must plan the distribution by trucks of loaded and empty containers to customers. The thesis addresses this type of vehicle routing problems, which are motivated by a real case study occurred during the collaboration with a carrier that operates in the Mediterranean Sea in door-to-door modality. The carrier manages a fleet of trucks based at the port. Trucks and containers are used to service two types of transportation requests, the delivery of container loads from the port to import customers, and the shipment of container loads from export customers to the port. The thesis addresses two problems which differ in the composition of the fleet of trucks. The first problem involves a heterogeneous fleet of trucks that can carry one or two containers. We present a Vehicle Routing Problem with backhauls, load splits into multiple visits, and the impossibility to separate trucks and containers during customer service. Then, we formalize the problem by an Integer Linear Programming formulation and propose an efficient meta-heuristic algorithm able to solve it. The meta-heuristic determines the initial solution by a variant of the Clarkeand-Wright algorithm, and improves it by several local search phases, in which both node movements and truck swaps are implemented. The second problem involves a homogeneous fleet of trucks that can carry more than a container. As a consequence, the identification of routes can be more difficult. We present and formalize the associated Vehicle Routing Problem by an Integer Linear Programming formulation. Then we propose an efficient adaptive guidance meta-heuristic algorithm able to solve it. The meta-heuristic determines an initial feasible solution by a Tabu Search step, and next improves this solution by appropriate adaptive guidance mechanisms

    Models and algorithms for the empty container repositioning and its integration with routing problems

    Get PDF
    The introduction of containers has fostered intermodal freight transportation. A definition of intermodality was provided by the European Commission as โ€œa characteristic of a transport system whereby at least two different modes are used in an integrated manner in order to complete a door-to-door transport sequenceโ€. The intermodal container transportation leads to several benefits, such as higher productivity during handling phases and advantages in terms of security, losses and damages. However, the distribution of containers comes with a drawback: due to directional imbalances in freight flows, some areas tend to accumulate unnecessary empty containers, while others face container shortages. Several planning models were developed for carriers in order to manage both loaded and empty containers profitably. However, they were built to operate under normal circumstances, neglecting the fact that networks are increasingly affected by both uncertainty and vulnerability, which may result in disruptions. The thesis aims to survey whether the impact of uncertainty can be mitigated by a stochastic programming approach, in which disruptions and normal operations are both foreseen as possible futures or scenarios. This approach is carried out by a multi-scenario optimization model in which scenarios are linked by non-anticipativity conditions. The empty container repositioning becomes even more challenging and difficult when integrated with routing problems. In fact, carriers often face problems in which they must determine simultaneously how many empty containers are carried by a fleet of vehicles and which routes must be followed by these vehicles. These problems typically arise in inland networks, in which one must plan the distribution by trucks of loaded and empty containers to customers. The thesis addresses this type of vehicle routing problems, which are motivated by a real case study occurred during the collaboration with a carrier that operates in the Mediterranean Sea in door-to-door modality. The carrier manages a fleet of trucks based at the port. Trucks and containers are used to service two types of transportation requests, the delivery of container loads from the port to import customers, and the shipment of container loads from export customers to the port. The thesis addresses two problems which differ in the composition of the fleet of trucks. The first problem involves a heterogeneous fleet of trucks that can carry one or two containers. We present a Vehicle Routing Problem with backhauls, load splits into multiple visits, and the impossibility to separate trucks and containers during customer service. Then, we formalize the problem by an Integer Linear Programming formulation and propose an efficient meta-heuristic algorithm able to solve it. The meta-heuristic determines the initial solution by a variant of the Clarkeand-Wright algorithm, and improves it by several local search phases, in which both node movements and truck swaps are implemented. The second problem involves a homogeneous fleet of trucks that can carry more than a container. As a consequence, the identification of routes can be more difficult. We present and formalize the associated Vehicle Routing Problem by an Integer Linear Programming formulation. Then we propose an efficient adaptive guidance meta-heuristic algorithm able to solve it. The meta-heuristic determines an initial feasible solution by a Tabu Search step, and next improves this solution by appropriate adaptive guidance mechanisms

    Port choice: A frequency-based container assignment model

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    The process of containerization has connected the world with a cost-effective freight service, successfully forming a competitive global market. Mixed freight shipping has changed dramatically due to containerization and globalization. The port system has experienced a tough time keeping pace with globalisation in terms of its roles and functions in liner shipping. Consequently, port choice has become a challenging problem to analyse with many stakeholders and complex circumstances. The literature formulating the basis of this maritime container assignment model can be identified as a combination of port choice modelling, a freight flow model and empty container repositioning. It is observed that the maritime container assignment problem shares a greater affinity with transit assignment than with traffic assignment conventionally applied freight in the four step approach, because containers are generally carried by shipping lines which operate services on fixed routes or port rotations. A model capable of representing full and empty container flows at a global level would be useful to almost every stakeholder in the container liner shipping industry, such as shippers, shipping lines, port authorities, terminal operating companies, regional and national planning authorities, marine insurance companies, and others. The classic frequency-based transit assignment approach of Spiess and Florian is transferred and applied to maritime containers as the foundation for a global maritime container assignment model. The first version of this model assigned full and empty containers to routes to minimise expected travel time, which consists of sailing time between ports and dwell time at intermediate transhipment ports. Service frequency and port capacity influence the pattern of full and empty container flows and therefore port choice. In this thesis, the model is further developed to fit the reality of container liner shipping by minimising expected cost rather than expected travel time. The objective is now to assign container flows to routes to minimize the sailing costs and expected dwell costs at the origin port and intermediate transhipment ports. The constraints included are extended to include the maximum number of containers each route can carry. Finally, the capabilities of the cost-based container assignment model are explored through a case study of the Europe-Far East trade lane. A range of strategy and policy options, such as a shipping line planning a new route or modifying an existing route and a port authority considering expansion, are simulated. A possible approach to model validation through independent data is proposed. Recommendations for future research are provided at the end of the thesis. Many aspects are covered in the thesis; an origin-destination matrix estimation, automated virtual (task) network construction from routes and schedules, improvements to the probability distribution used for ship arrivals, a validation procedure, and model extension from port-to-port movements to door-to-door container movements
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