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

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