Selective maintenance scheduling under stochastic maintenance quality with multiple maintenance actions

• Published in: International journal of production research Vol. 56; no. 23; pp. 7160 - 7178
• Main Authors: Duan, Chaoqun, Deng, Chao, Gharaei, Abolfazl, Wu, Jun, Wang, Bingran
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 02.12.2018; Taylor & Francis LLC
• Subjects: Component reliability; Computer simulation; conditional reliability; Maintenance costs; Maintenance management; Missions; multiple maintenance actions; Preventive maintenance; Probability theory; Scheduling; selective maintenance scheduling; Series (mathematics); Simulated annealing; simulated annealing algorithm; stochastic maintenance quality
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2018.1436789

Many systems are required to perform a series of missions with finite breaks between any two consecutive missions. To improve the probability of system successfully completing the next mission, maintenance action is carried out on components during the breaks. In this work, a selective maintenance model with stochastic maintenance quality for multi-component systems is investigated. At each scheduled break, a set of maintenance actions with different degrees of impact are available for each component. The impact of a maintenance action is assumed to be random and follow an identified probability distribution. The corresponding maintenance cost and time are modelled based on the expected impact of the maintenance action. The objective of selective maintenance scheduling is to find the cost-optimal maintenance action for each component at every scheduled break subject to reliability and duration constraints. A simulated annealing algorithm is used to solve the complicated optimisation problem where both multiple maintenance actions and stochastic quality model are taken into account. Two illustrative numerical examples and a real case study have been solved to demonstrate the performance of the proposed approach. A comparison with deterministic maintenance shows the importance of considering the proposed stochastic quality in selective maintenance scheduling.