A Joint Order Cost Optimization Model for Multi-Item Spare Parts Manufacturing Systems Considering the Requirement of Support Probability

Reducing the comprehensive cost of spare parts is one of the main objectives of cost-optimized design for manufacturing systems (MSs). For multi-item MSs, there are usually more variables in the composition of spare parts costs, such as purchase and inventory costs. Besides, the coupling and interaction between these variables have an impact on the final spare parts cost, forming a more complex combinatorial optimization problem. Moreover, the ordering activities of spare parts are scattered in the time window between each period, and the order quantity and timing can significantly impact the cost. To solve this problem, this paper proposes a joint ordering cost optimization model that considers the requirement of support probability. In this paper, a mixed integer linear programming model (MILP) is established for the specific problem. Finally, numerical examples are synthesized based on actual records to verify the feasibility and rationality of the model. The results also show that the proposed model significantly reduces the total spare parts costs of MSs.