291 research outputs found

    Liner Service Network Design

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    Optimization in liner shipping

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    Strategic maritime container transport design in oligopolistic markets

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    AbstractThis paper considers the maritime container assignment problem in a market setting with two competing firms. Given a series of known, exogenous demands for service between pairs of ports, each company is free to design a liner service network serving a subset of the ports and demand, subject to the size of their fleets and the potential for profit. The model is designed as a three-stage complete information game: in the first stage, the firms simultaneously invest in their fleet; in the second stage, they individually design their networks and solve the route assignment problem with respect to the transport demand they expect to serve, given the fleet determined in the first stage; in the final stage, the firms compete in terms of freight rates on each origin-destination movement. The game is solved by backward induction. Numerical solutions are provided to characterize the equilibria of the game

    New Concept of Container Allocation at the National Level: Case Study of Export Industry in Thailand

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    This paper presents container allocation technique of which minimizing the total opportunity loss of an export industry in Thailand. This new allocation concept applies as a strategic management tools at the national level since it is consistent to the characteristics of the container supply chain management in Thailand. The first section of this paper presents the review of facts and problems of container supply chain management. It reveals that containerization system is significant to the international trade as it holds good characteristics of sea transportation. It can transport a lot of products while minimize the damage of goods. Supply chain management of the containerization system presents and shows that there are four main players in managing the container โ€“ principal, port, container depot, and customer. After an intensive review of containerization systemโ€™s problem, the most common problem that all parties have encountered is an imbalance between demand and supply of container. The well-known solution to the stated problem is relocation of containers between various places using optimization technique, which aims to minimize operation cost. Indeed, those solutions are unable solve the containerization systemโ€™s problem in Thailand: lacking their own fleets: having no bargaining power in relocating container between areas as needed. In the present, many of Thai exporters face with losses of sales or profit because they cannot find enough or proper containers to transport their goods to the customer. The authors, therefore, have seen that those problems need to be strategically solved by the government. The limited number of containers must be properly allocated to the exporter with regard to the minimum losses to the economics of the country. The main contributions of this paper are two folds. First, the opportunity losses of the various export industry are indicated when lack of containers, Second, the mathematical model has been formulated using linear programming technique with several constraints, such as, demand, supply, obsolete time, operating cost, lead time etc. The authors hope that the new concept presented in this paper will provide the great contribution for other countries, which face the same problem of Thailand. Keywords: Container Management, Opportunity Loss, Allocation Problem, Optimization, International Trad

    A multi-stage approach for empty container repositioning under coordination among linear carriers

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    This paper studies the empty container repositioning (ECR) problem considering the exchange of slots and empty containers among liner shipping companies. It is common for an individual shipping company to seek an optimal solution for ECR and cargo routing to maximize its own benefits. To achieve cooperation among shipping companies, a multi-stage solution strategy is proposed. With the inverse optimization technique, the guide leasing prices of slots and empty containers among shipping companies are derived considering the schedule of vessels and cargo routing. Based on the guide leasing price, a cooperative model is formulated to minimize the total cost, which includes the transportation cost for laden containers, the inventory holding cost, the container leasing cost, and the repositioning cost. All the involved shipping companies are expected to follow the best solution of ECR and cargo routing to achieve a cooperative and stable optimum. A real-world shipping network operated by three liner shipping companies is used as a case study with promising numerical results

    ๊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

    Optimization of East Med service of ZIM

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    Risk management in liner ship fleet deployment: a joint chance constrained programming model

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    This paper provides a tangible methodology to deal with the liner ship fleet deployment problem aiming at minimizing the total cost while maintaining a service level under uncertain container demand. The problem is first formulated as a joint chance constrained programming model, and the sample average approximation method and mixed-integer programming are used to deal with it. Finally, a numerical example of a liner shipping network is carried out to verify the applicability of the proposed model and solution algorithm. It is found that the service level has significant effect on the total cost
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