An optimization model for product returns using genetic algorithms and artificial immune system

Current environmental issues emerging in the world are reflected in the environmental legislation of several countries. Because environmental issues are important, industries actively seek ways in which to reduce their environmental footprint. One effective method is through the use of reverse logistics. Reverse logistics is the concept of reusing used products in order to reduce wastes and to increase an industry's environmental performance and resulting profits. Stock selection, transportation, centralized collection, data collection, refurbishing, and remanufacturing are some of the more commonly utilized reverse logistic operations. An effective reverse logistics network is essential for increasing the flow of goods from customers to producers. The objective of this paper is to develop a multi-echelon reverse logistics network for product returns to minimize the total reverse logistics cost, which consists of renting, inventory carrying, material handling, setup, and shipping costs. Industries need to give more attention to the task of collecting used products from customers and establishing collection facilities. In this study, a mixed integer non-linear programming (MINLP) model is developed to find out the number and location of initial collection points and centralized return centers required for an effective return and collection system, and also the maximum holding time (collection frequency) for aggregation of small volumes of returned products into large shipments. Two solution approaches, namely genetic algorithm and artificial immune system, are implemented and compared. The usefulness of the proposed model and algorithm are demonstrated via an illustrative example.