Solving a multi-stage multi-product solid supply chain network design problem by meta-heuristics

This paper presents an effective optimization method based on meta-heuristics algorithms for the design of a multi-stage, multi-product solid supply chain network design problem. First, a mixed integer linear programming model is proposed. Second, because the problem is an NP-hard, three meta-heuristics algorithms, namely Differential Evolution (DE), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA), are developed for the first time for this kind of problem. To the best of our knowledge, neither DE, nor PSO, nor GSA have been considered for the multi-stage solid supply chain network design problems. Furthermore, the Taguchi experimental design method is used to adjust the parameters and operators of the proposed algorithms. Finally, to evaluate the impact of increasing the problem size on the performance of our proposed algorithms, different problem sizes are applied and the associated results are compared with each other.