A multi-objective evolutionary approach for integrated production-distribution planning problem in a supply chain network

Integrated production-distribution planning (PDP) is one of the most important approaches in supply chain networks. We consider a supply chain network (SCN) to consist of multi suppliers, plants, distribution centers (DCs), and retailers. A bi-objective mixed integer linear programming model for integrating production-distribution designed here aim to simultaneously minimize total net costs in supply chain and transfer time of products for retailers. From different terms of evolutionary computations, this paper proposes a Pareto-based meta-heuristic algorithm called multi-objective simulated annealing (MOSA) to solve the problem. To validate the results obtained, a popular algorithm namely non-dominated sorting genetic algorithm (NSGA-II) is utilized as well. Since the solution-quality of proposed meta-heuristic algorithm severely depends on their parameters, the Taguchi method is utilized to calibrate the parameters of the proposed algorithm. Finally, in order to prove the validity of the proposed model, a numerical example is solved and conclusions are discussed.