Multi-Period Spare Parts Supply Chain Network Optimization under (T, s, S) Inventory Control Policy with Improved Dynamic Particle Swarm Optimization

Abstract

Highlights: What are the main findings? An extended (T, s, S) inventory control strategy is utilized to manage spare parts in customer nodes; A dynamic nonlinear programming model is developed for optimizing inventory control decisions and spare part supply decisions; An improved self-adaptive dynamic migrating PSO is proposed in which a novel environment change detection and response strategy is applied. What are the implications of the main findings? Solving the joint optimization problem of spare part management and spare part supply chain network optimization under multiple supply periods; The improved dynamic particle swarm optimization algorithm has better computation efficiency and performance than the traditional algorithm. Spare parts are the critical operation asset for ensuring a production line keeps going, which significantly improves the performance of manufacturing enterprises. This article pays attention to the joint optimization of spare part management and spare part supply chain network optimization in multiple supply periods. An extended (T, s, S) inventory control strategy is utilized to manage spare parts in customer nodes which can determine supply time, consumption and demand. In this spare part supply chain, the supply environment is different in different periods, so the mathematical model and solution method should be able to respond to and detect the environment change quickly. Hence, a dynamic nonlinear programming model is developed for optimizing inventory control decisions and spare part supply decisions so as to minimize the total cost. Furthermore, an improved self-adaptive dynamic migrating particle swarm optimization algorithm is proposed to solve the optimization problem. In this algorithm, a novel environment change detection and response strategy is applied to deal with the dynamic period in the spare part supply chain network. The results obtained show that the improved algorithm improves the computation time by eight percent and has better computational efficiency compared with the traditional algorithm.