LAUSR

This paper investigates the multi-period and multi-echelon spare parts supply optimization problem. The periodic review order-up-to-S inventory policy is considered to determine the supply timing and the spare parts consumption. The spare parts consumption in lead time of each customer is calculated by the process of spare parts supply. A dynamic optimization mathematical model is developed to find the optimal supply scheme in each period with the objective of minimum total costs. In the dynamic optimization model, the parameters are changed with time. An improved particle swarm optimization algorithm is proposed to solve this dynamic problem. In the proposed algorithm, a change detector and a change response strategy are used to handle the change of optimization environment. We also adopt a self-adaption neighborhood search strategy to improve the ability of exploration and exploitation of the algorithm. Numerical experiment shows that the proposed strategy can improve the performance of algorithm greatly, and the model and solution method in this paper can be applied to optimize the spare parts supply in various scenarios.