考虑紧急订货的系统备件费用优化建模与求解

Reducing the comprehensive cost of spare parts is one of the main objectives of cost optimization research in the support phase of complex systems. Especially in uncertain scenarios, such as large-scale equipment training and batch production of newly developed products, the demand for spare parts and the associated cost changes are harder to predict. Therefore, a Markov chain can be used to predict demand and complete the ordering process in combination with the emergency order strategy. However, there is a coupling effect between regular and emergency orders, which affects the total spare parts cost, creating a complex optimization problem. To address this issue, this paper proposes an optimization model and solves it using mixed integer linear programming. Finally, the model is applied and validated using data from a real-world scenario. The results show that the total cost is reduced by 295,600 yuan, with a 40.63% reduction in inventory storage costs. This demonstrates that the proposed model effectively lowers spare parts costs, improves system economic efficiency, and provides technical support for optimizing spare parts costs in complex systems.