A Modal Interval Based Genetic Algorithm for Closed-loop Supply Chain Network Design under Uncertainty

摘要

This paper proposes a modal interval based genetic algorithm to solve the closed-loop supply chain network configuration puzzle under uncertainty. In this special network, the retailer dominates the collection and remanufacturing activities, and sets up dedicate remanufacturing centers to remanufacture the components disassembled from the end of life excavators. This paper applies the modal intervals to characterize the uncertain parameters and combine the modal interval analysis with the genetic algorithm to solve the proposed modal interval linear programming problem. Moreover, three different decision criteria are adopted to analyze the optimal decisions of the remanufacturer. The results confirm that the proposed method can successfully determine the location of different facilities and the allocation of the products and components.