Spare Parts Closed-Loop Logistics Network Optimization Problems: Model Formulation and Meta-Heuristics Solution

Abstract

Logistics network optimization is an important part of the spare parts allocation problem. In recent years, reverse logistics has greatly increased the efficiency of the supply chain. However, it also increases the difficulty of mathematical modeling and solving. In order to solve the network optimization problem of spare parts, a multi-period closed-loop logistics network is established. The practical problem is described as a mixed nonlinear integer programming model with multi-objective and multi-constraint. An improved multi-objective ant lion algorithm is proposed to solve this model. In the proposed algorithm, Levy flight and the quasi-opposites-based learning strategy are used to improve the performance of the algorithm. The numerical simulation shows that the convergence and distribution of the result of the proposed algorithm are promoted. Finally, the mathematical model is solved by the proposed algorithm, and a sensitivity analysis is carried out. The results show that, first, the proposed closed-loop supply network is superior to the traditional forward logistics network. Second, the improved ant lion algorithm is more effective than a basic ant lion algorithm and other classical algorithms.