Modeling and Optimization of Maintenance Strategies in Leasing Systems Considering Equipment Residual Value

This study addresses the limitations of existing maintenance decision-making approaches that predominantly rely on single-objective strategies for leased production systems with complex series–parallel configurations. An integrated opportunity-based adaptive maintenance strategy is proposed, and a multi-objective optimization model incorporating multiple maintenance alternatives is developed. First, a proportional hazards model to characterize the degradation-dependent failure rates of key components is used to characterize equipment failure rates, which inform the selection of maintenance actions. Second, the effects of virtual age and maintenance strategies on the residual value of leased equipment are analyzed, leading to the formulation of a net residual value model from the lessor’s perspective. Simultaneously, a customer cost model is established by considering both product quality loss and downtime loss. Finally, the NSGA II algorithm is employed to solve the proposed multi-objective optimization model, yielding optimal preventive maintenance intervals, opportunistic maintenance thresholds, preventive maintenance thresholds, and the corresponding Pareto front. A case study illustrates the strategy’s superior flexibility and practical applicability, with its effectiveness further validated through comparative analysis against traditional maintenance strategies.

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Publication Details

Journal
Modelling—International Open Access Journal of Modelling in Engineering Science
Published
2025-06-24
DOI
https://doi.org/10.3390/modelling6030052
Primary Topic
Reliability and Maintenance Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Modeling and Optimization of Maintenance Strategies in Leasing Systems Considering Equipment Residual Value

S. Shao, Yujia Wang, Bozhi Deng, Guoqing Cheng
Modelling—International Open Access Journal of Modelling in Engineering Science
Reliability and Maintenance Optimization
article

Modeling and Optimization of Maintenance Strategies in Leasing Systems Considering Equipment Residual Value

S. Shao, Yujia Wang, Bozhi Deng, Guoqing Cheng
article en

Abstract

This study addresses the limitations of existing maintenance decision-making approaches that predominantly rely on single-objective strategies for leased production systems with complex series–parallel configurations. An integrated opportunity-based adaptive maintenance strategy is proposed, and a multi-objective optimization model incorporating multiple maintenance alternatives is developed. First, a proportional hazards model to characterize the degradation-dependent failure rates of key components is used to characterize equipment failure rates, which inform the selection of maintenance actions. Second, the effects of virtual age and maintenance strategies on the residual value of leased equipment are analyzed, leading to the formulation of a net residual value model from the lessor’s perspective. Simultaneously, a customer cost model is established by considering both product quality loss and downtime loss. Finally, the NSGA II algorithm is employed to solve the proposed multi-objective optimization model, yielding optimal preventive maintenance intervals, opportunistic maintenance thresholds, preventive maintenance thresholds, and the corresponding Pareto front. A case study illustrates the strategy’s superior flexibility and practical applicability, with its effectiveness further validated through comparative analysis against traditional maintenance strategies.

Modelling—International Open Access Journal of Modelling in Engineering ScienceVol. 6(3)
Shanghai Ocean University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 83%
Reliability and Maintenance Optimization
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Modeling and Optimization of Maintenance Strategies in Leasing Systems Considering Equipment Residual Value — S. Shao, Yujia Wang, et al. · Modelling—International Open Access Journal of Modelling in Engineering Science (2025) | TGRS Research Map | TGRS