Optimal maintenance strategy for multi-stage leased production systems considering customer satisfaction

Abstract

For multi-process leasing systems affected by aging and wear, this paper proposes a maintenance strategy that comprehensively considers production, quality, and maintenance management. Firstly, to accurately describe the degradation and failure mechanisms of components within the system, the Gamma process is employed to model the stochastic degradation of core equipment components, while the proportional hazards model is used to analyze the impact of component degradation on the risk rates of other components. Addressing the limitation of maintenance resources, the importance of equipment is classified, and a monotonic increasing relationship between product defect rates and degradation levels is introduced. A quality control strategy is proposed, involving the preventive replacement of core components in high-defect-rate processes to reduce the system’s overall defect rate. Secondly, the maintenance strategy comprehensively considers the multiple effects of preventive maintenance, preventive replacement, and post-failure repair on the cost rate, establishing an imperfect maintenance cost model for lessors. Based on the economic production quantity model and considering market demand volatility, customer satisfaction is modeled by incorporating product quality, stockout losses, and inventory cost indicators. Finally, Monte Carlo simulation and simulated annealing algorithms are adopted to optimize the bilateral cost rate, yielding the optimal production batch size, preventive maintenance parameters, and quality control thresholds. Sensitivity analysis is conducted to verify the robustness of the model. This optimization strategy enhances the operational efficiency of the equipment leasing system, reduces bilateral cost rates, and improves customer satisfaction.