Supplier Selection and Order Allocation Under Disruption: Multi-Objective Evolutionary Algorithms

Abstract

Disruption is one of the critical issues that affect the performance and costs of supply chain management. The appropriate adjusting of supply chain disruptions is considered as a competitive privilege for companies. Hence, this paper aims to improve an optimization approach to select suppliers and allocate the proper quota of order to each one considering supplier disruption. A Multi-Objective Mixed Integer Linear Programming (MOMILP) is proposed model with five objective functions, minimize costs of the transaction and supplying, the percentage of delayed products, and the percentage of returned products, as well as maximize capabilities of orders tracking by customers. Strength Pareto Evolutionary Algorithm-II (SPEA-II) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) are developed to settle this problem. The efficiency of the solution algorithms is investigated based on four criteria for eight computational experiments. The results indicate the SPEA-II algorithm provides better solutions in comparison with the NSGA-II algorithm.