A multi-objective integrated production-allocation and distribution planning problem of a multi-echelon supply chain network: two parameter-tuned meta-heuristic algorithms.

Supply chain management (SCM) is a subject that has found so much attention among different commercial and industrial organizations due to the competing environment of products. Therefore, integration of constituent element of this chain is a great deal. This paper proposes a multi objective production-allocation and distribution planning problem (PADPP) in a multi echelon supply chain network. We consider multi suppliers, manufacturers, distribution centers, customers, raw materials and products in the multi-time periods. Three objective functions are minimizing the total costs of supply chain between all echelons, the delivery time of products to customers with decrease flow time in the chain, and the lost sales of products in distribution centers. Since the under investigation model is proved as a strongly NP-hard problem, we solve it with two meta-heuristics algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO). Also, to justify the performance and efficiency of both algorithms, a variable neighborhood search (VNS) is addressed. The design of experiments and response surface methodologies (RSM) have been utilized to calibrate the parameters of both algorithms. Finally, computational results of the algorithms are assessed on some classified generated problems. Statistical tests indicate that proposed GA and PSO algorithms have a better performance in solving proposed model compared to VNS.