A genetic algorithm approach on reverse logistics optimization for product return distribution network

Traditionally, product returns have been viewed as an unavoidable cost of distribution systems. Up to now there are few studies to address the problem of determining the number and location of centralized product return centers where returned products from retailers or end-customers are collected for manufacturers' or distributors' repair facilities while considering the distribution system. To fill the void in such a line of research, this paper proposes a nonlinear mixed-integer programming model and a genetic algorithm that can solve the distribution problem with forward and reverse logistics simultaneously. Compared with a partly enumeration method, the numerical analysis shows the effectiveness of the proposed model and its genetic algorithm approach.