Optimization Design of Multi-layer Logistics Network Based on Self-Adaptive Gene Expression Programming

Abstract

In order to solve the multi-layer logistics network optimization problem of modern enterprises, a mixed integer programming model with minimum total cost is established by considering the inventory cost and transportation cost and the switching state of the transit logistics nodes. According to the characteristics of multi-layer logistics network optimization problem, the gene expression programming with the characteristics of multi-gene structure is adopted, and the self-adaptive evolution mechanism is introduced to dynamically adjust the genetic operator. A self-adaptive gene expression programming algorithm based on Prüfer coding (SA-GEP) is proposed to solve the model. The algorithm introduces the insertion operator based on the original genetic operator of gene expression programming. The experimental results show that compared with STD-GEP algorithm and EC algorithm, the optimization effect of SA-GEP algorithm is more significant, which greatly improves the performance of the algorithm, and verifies the feasibility of the model and the effectiveness of the algorithm.