Forecasting Demand and Optimization of Spare Parts for Maintenance and Repair of Automobiles

Abstract

Emergency engineering equipment plays a key role in implementing emergency repair strategies to ensure timely and efficient maintenance of vehicles. However, random failures of this equipment may occur during operation, necessitating the development of adequate maintenance strategies and spare parts inventory management to ensure its continuous operation. Moreover, the urgency level of repair requests can vary significantly, affecting the order in which they are serviced. Existing studies rarely consider the impact of urgency levels and request priorities on the repair process. To address these gaps, this paper proposes a joint optimization model of maintenance and spare parts inventory strategies for emergency engineering equipment used in vehicle repair, taking into account request priorities. The model includes two types of equipment with different repair speeds: one provides expedited service for urgent requests, while the other handles regular service. The Markov process embedding method is employed to derive probabilistic reliability indicators of the system. To maximize system availability, the number of maintenance engineers and the spare parts inventory strategy are optimized using the branch-and-bound method. Practical examples demonstrate the effectiveness of the proposed model and methods in vehicle repair scenarios.