A Simulated Annealing Genetic Algorithm for Logistics Distribution Problem in Community Scenario

Yifan Yang, Jie Zhu, Haiping Huang, Yixuan Li

Abstract: To improve the flexibility and efficiency of the logistics distribution process, a new logistics distribution model, namely Community Logistics System(CLS) model, is established. In this paper, we consider that it is impossible to transport every package to the express cabinet closest to the customer. Hence, the optimizing objective of this problem is to minimize the total fetching distance. In addition, considering the capacity constraints of express cabinets, a reasonable allocation strategy is designed. We propose a Simulated Annealing Genetic (SAG) algorithm to solve the problem. In order to promote the efficiency and the optimization effect, we generate the first generation population by using Simulated Annealing algorithm instead of using random selection as in most case. By comparing with other two heuristic algorithm (SA and GA), the proposed algorithm is proved to be robust and effective for the research problem.