Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study

被引：223
作者：
Hasani, Aliakbar [1 ]
Khosrojerdi, Amirhossein [2 ]
机构：
[1] Univ Shahrood, Sch Ind Engn & Management, Shahrood, Iran
[2] Univ Oklahoma, Sch Ind & Syst Engn, Norman, OK 73019 USA
来源：
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW | 2016年 / 87卷
关键词：
Global supply chain network design; Robust optimization; Resilience strategies; Disruption management; Parallel memetic algorithm; Fitness landscape analysis; VARIABLE NEIGHBORHOOD SEARCH; GENETIC-ALGORITHMS; RISK-MANAGEMENT; MODEL; LOGISTICS; FACILITY; SYSTEM; TRANSPORTATION; LOCATION; PERFORMANCE;
D O I：
10.1016/j.tre.2015.12.009
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
A mixed-integer, non-linear model is developed for designing robust global supply chain networks under uncertainty. Six resilience strategies are proposed to mitigate the risk of correlated disruptions. In addition, an efficient parallel Taguchi-based memetic algorithm is developed that incorporates a customized hybrid parallel adaptive large neighborhood search. Fitness landscape analysis is used to determine an effective selection of neighborhood structures, while the upper bound found by Lagrangian relaxation heuristic is used to evaluate quality of solutions and effectiveness of the proposed metaheuristic. The model is solved for a real-life case of a global medical device manufacturer to extract managerial insights. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码：20 / 52
页数：33
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