Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

공컨테이너관리 기법을 활용한 효율적인 컨테이너 공급망

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 문일경.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.해상 수송이 주도함으로써 전 세계 무역량이 급증하기 때문에 회사 소유 컨테이너는 컨테이너 흐름을 관리하는 데 많은 노력을 기울여야 한다. 이를 위해 공 컨테이너 관리 전략을 포괄적으로 구현하고 효율적인 수송 비용 및 시간 절감 방식으로 컨테이너 흐름을 원활히 하여 관련 총비용을 최소화하는 동시에 고객의 수요를 최대한 충족하게 된다. 그러나 현실에서는 높은 불확실성 때문에 컨테이너 흐름에 대한 많은 주요한 이슈가 보편적으로 존재하고 효율적인 컨테이너 공급망 구축을 방해한다. 본 논문에서는 이러한 이슈에 대해 전반적으로 논의하고 적절한 해법과 함께 수리 모형을 제공한다. 이를 위해 세 가지 유형의 컨테이너 공급망을 다룬다. 먼저 (i) 양방향 네 단계 컨테이너 공급망, (ii) 분권화 및 중앙 집중화 정책에 따른 적∙공 컨테이너 공급망; 그리고 (iii) disruption 상황 속에서 신뢰성을 고려하는 컨테이너 공급망이다. 본 논문에서 제시한 세 가지 모형은 컨테이너 공급망에 내재 된 높은 위험을 직접 다루며 계산 실험은 해운 회사의 경영진이나 관계자를 위해 주요한 관리 인사이트를 제공한다. (i)의 경우, 두 지역 간 양자 무역을 위한 양방향 네 단계 컨테이너 공급망에서 공 컨테이너 관리 전략을 연구한다. 이 전략은 환적으로 인한 높은 해상 운송 비용과 긴 배송 시간을 줄일 수 있다. 또한, 직항 수송의 영향을 조사하여 선택된 항구 중 재배치 할 공 컨테이너 수, 임대 컨테이너 수, 두 지역의 수출업자와 수입업자의 적∙공 컨테이너 대한 수요를 만족하기 위한 경로 선택을 결정하게 된다. APSO 및 휴리스틱을 기반으로 하는 하이브리드 해법을 제시하며 비교 실험을 하였다. (ii)의 경우 최적 devanning time 결정을 목표로 공 컨테이너의 반환 지연과 해당 수수료 부과 결정 프로세스와 관련하여 서로 다른 세 가지 시나리오를 기반으로 적∙공 컨테이너 공급망 모형을 제시한다. 각 유형의 정책적(분권화 및 중앙 집중화) 효과는 정시 반환율을 포함한 주요 성능 측정을 고려하는 계산 실험을 통해 결정된다. 이러한 정책 실행에 대한 유용한 관리 인사이트는 민감도 분석 및 비교 연구의 결과에서 도출한다. (iii)의 경우, 본 논문은 컨테이너 네트워크 흐름을 기반으로 하는 신뢰성 모형을 개발하는 동시에 아크 및 노드 failure가 있을 때 street-turn 및 공 컨테이너 재배치 비용을 포함한 기대 총 비용을 구한다. 중단이 컨테이너 공급망 네트워크에 미치는 영향을 분석하기 위해 민감도 분석을 수행했으며 disruption 비용을 결정하기 위해 벤치마크 모형을 활용한다. 더불어 신뢰성을 고려한 컨테이너 네트워크 흐름을 구축하고 신뢰성을 유지하는 방법에 대한 관리적 인사이트도 제공한다.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 〖pn〗_{f1} and 〖pn〗_{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 국문초록 157 감사의 글 160Docto

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