Growing environmental and economical concern has led to increasing attention towards management of product return flows. An effective and efficient reverse logistics network enables companies to gain more profit and customer satisfaction. Consequently, the reverse logistics network design problem has become a critical issue. After a brief introduction to the basic concepts of reverse logistics, the authors formulate a new integrated multi-stage, multi-period, multi-product reverse logistics model for a remanufacturing system where the inventory is considered. Two objectives, minimization of the costs and maximization of coverage, are addressed. Since such network design problems belong to a class of NP-hard problems, a multi-objective genetic algorithm and a multi-objective evolutionary strategy algorithm are developed in order to find the set of non-dominated solutions. Finally, the model is tested on test problems with different sizes, and the proposed algorithms are compared based on the number, quality, and distribution of non-dominated solutions that belong to the Pareto front.
