1 research outputs found
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곡νκ³Ό, 2021. 2. λ¬ΈμΌκ²½.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.ν΄μ μμ‘μ΄ μ£Όλν¨μΌλ‘μ¨ μ μΈκ³ 무μλμ΄ κΈμ¦νκΈ° λλ¬Έμ νμ¬ μμ 컨ν
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μ΄λ λ€νΈμν¬ νλ¦μ ꡬμΆνκ³ μ λ’°μ±μ μ μ§νλ λ°©λ²μ λν κ΄λ¦¬μ μΈμ¬μ΄νΈλ μ 곡νλ€.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 γpnγ_{f1} and γpnγ_{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
κ΅λ¬Έμ΄λ‘ 157
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