Resilient Supply Chain Optimization Considering Alternative Supplier Selection and Temporary Distribution Center Location

Na Wang, Jingze Chen and Hongfeng Wang ()
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Na Wang: Department of Basic Computing and Mathematics, Shenyang Normal University, Shenyang 110034, China
Jingze Chen: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Hongfeng Wang: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Mathematics, 2023, vol. 11, issue 18, 1-22

Abstract: The global supply chain is facing huge uncertainties due to potential emergencies, and the disruption of any link may threaten the security of the supply chain. This paper considers a disruption scenario in which supply disruption and distribution center failure occur simultaneously from the point of view of the manufacturer. A resilient supply chain optimization model is developed based on a combination of proactive and reactive defense strategies, including manufacturer’s raw material mitigation inventory, preference for temporary distribution center locations, and product design changes, with the objective of obtaining maximum expected profit. The proposed stochastic planning model with demand uncertainty is approximated as a mixed integer linear programming model using Latin hypercube sampling (LHS), sample average approximation (SAA), and scenario reduction (SR) methods. In addition, an improved genetic algorithm (GA) is also developed to determine the approximate optimal solution. The algorithm ensures the feasibility of the solution and improves the solving efficiency through specific heuristic repair strategies. Numerical experiments are conducted to verify the application and advantages of the proposed disruption recovery model and approach. The experimental results show that the proposed resilient supply chain optimization model can effectively reduce the recovery cost of manufacturers after disruption, and the proposed approach performs well in dealing with related problems.

Keywords: resilient supply chain; disruption recovery; heuristic; genetic algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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