An Hybrid Discrete Particle Swarm Optimization Algorithm for Solving a Sustainable Remanufacturing Supply Chain Problem

We propose a hybrid evolutionary algorithm based on discrete Particle Swarm Optimization for solving a novel remanufacturing sustainable supply chain design problem. There is little research being done in mathematical modeling and solutions methods for these problems. The paper describes a NP-hard mixed-integer 0-1 model (MIP) for this remanufacturing sustainable problem in which given a network of three layers, we have to determine the number of remanufacturing facilities to be located at sites chosen from among a given set of candidate sites, to allocate sourcing facilities to remanufacturing facilities and remanufacturing facilities to demand facilities with a given demand. The objective is to minimize transportation, distribution and operation costs of the facilities. The heuristics combine the traditional binary PSO algorithm with greedyexchange strategies in order to improve the quality of the solutions. We report computational results for instances generated from known data test available in the literature.