Configuration Optimization Study, Based On Equipment Readiness Spare Parts

Maintenance plays an important role in keeping the availability of civilian and military equipments at a satisfactory level. With the increasing complexity of modern equipments, maintenance requires more and more effort and time, which is becoming a key factor that has great impact on equipment operational readiness. In a system, spare parts maintenance and support system (SPMSS) plays a core role. By analyzing the SPMSS, appropriate decisions could be made, the results of which may help to optimize the inventories of the maintenance system, resulting improvement of equipment availability. Current approaches to analyzing the SPMSS depended on the experience of decision makers, which would lead to the high support cost and low availability. When the spare parts couldn't have a reasonable allocation, it will lead to maintenance and support system running ineffective, the failure time of the equipment becomes longer, and finally impact on the use of equipment. Thereby in this paper, aiming to multi-levels spares parts management, we systematically study the SPMSS in order to provide a scientific method for the maintenance and support decision-making.Firstly, we focus on operational readiness of the equipment system, and build the model for spare parts allocation optimization of multi-echelon SPMSS. Then, we introduce the decision-maker's satisfactory degree to express operational readiness, which include the satisfaction of support cost and availability. Based on above work, we use fuzzy multi-objective optimization and genetic algorithm to make an optimized solution. The main work of this paper can be summarized as follows:1. We analyze the model of multi-echelon SPMSS, propose decision-maker's satisfactory degree to express operational readiness, and build the optimization model; then, on the basis of the research on the spares maintenance and support process, we build the SPMSS by simulation method.2. We build the single-component two-echelon SPMSS, and we make an optimized solution by fuzzy multi-objective optimization, which applies the decision-maker's satisfactory degree as the optimized target. 3. A multi-component two-echelon SPMSS has been built. Furthermore the model of spare parts maintenance and support simulation optimization is proposed, which could combine simulation and optimization method. By using this model, we could get an optimized solution with genetic algorithm, whose fitness function is the decision-maker's satisfactory degree.