Simulation-optimization techniques for closed-loop supply chain design with multiple objectives

This paper presents a methodology for determining the optimal supply chain design with economic, environmental and risk management considerations. A multi-objective model based on mixed integer programming is proposed seeking three objectives: First, to minimize the total cost of transportation and the costs associated to the use of intermediate nodes. Second, to minimize the risks of product losses in transportation. Third, to minimize the environmental impact of CO2 emissions produced by transportation and storage operations. The proposed model is solved with two approaches: First, a commercial solver to compute the Pareto-optimal set of solutions. Second, a simulation-based optimization approach that allows to obtain statistically different but efficient solutions such that the decision-maker will be able to trade-off objectives while considering only Pareto optimal solutions. Experiments on random instances demonstrate the capability of the models and methods