Logistics network design optimization based on differential evolution algorithm

The aim of this paper is to study a mixed integer nonlinear programming model including forward logistics and reverse logistics. The problem is how to select plants, stores and collection points to constitute a closed-loop network, and how to expend the chosen plants, stores and collection points. Differential evolution algorithm is applied to solve the problem. The differential evolution algorithm originally is for global optimization over continuous spaces. According to the characteristics of the model, mapping method is developed to handle discrete variables. This study offers the improved differential evolution algorithm that optimizes the forward and reverse logistics simultaneously. Result shows the algorithm has a rapid convergence rate.