Development of an optimization model for product returns using genetic algorithms and simulated annealing

Product returns have become a significant management issue and an unavoidable cost for a business. This situation made firms to consider the possibility of managing product returns in a more cost-efficient way. In this paper, we develop a mixed integer non-linear programming model of a three-stage logistics network to optimize the number and location of collection/inspection centers and recovery centers as well as collection frequency with the objective of minimizing the total costs which include the reverse logistics shipping costs and fixed costs of opening facilities. We propose two solution algorithms based on genetic algorithm (GA) and simulated annealing (SA). Numerical experiments are used to illustrate the effectiveness and efficiency of the proposed solution approaches and compare the results with the previous works.