TY - GEN
T1 - A quantum-inspired genetic algorithm for optimization of a joint-maintenance strategy on multi-component engineering systems
AU - Cao, Jinrong
AU - Wang, Xuan
AU - Tang, Diyin
N1 - Publisher Copyright:
Copyright © ESREL2020-PSAM15 Organizers.Published by Research Publishing, Singapore.
PY - 2020
Y1 - 2020
N2 - A joint-maintenance strategy is proposed for multi-component engineering systems. It achieves the goal of reducing maintenance cost by performing preventive maintenance in groups. Two kinds of preventive maintenance operations are considered, referred to as minor maintenance and major maintenance, renewing each Shop Replaceable Unit (SRU) and the Least Replaceable Unit (LRU) respectively. The focal points of strategy mainly includes three part of costs: (i) cost of maintenance, (ii) cost of working consumption and (iii) cost of economic dependence. A quantum-inspired genetic algorithm (QGA) is proposed to find the optimum major maintenance cycle for LRU and the recurrence rate of minor maintenance for each SRU. The main idea focuses on that significant maintenance cost could be saved by performing preventive maintenance of some SRUs together. To support the optimization of two numerical types at the same time, hybrid coding is adopted in this algorithm to represent feasible solutions. Special evolutionary operations are developed to increase its diversity. It's shown that by performing preventive maintenance of some SRUs together, significant maintenance cost could be saved. A numerical case study is conducted and the experiment results demonstrates that the proposed maintenance strategy and corresponding optimization algorithm is effective in cutting the average maintenance costs.
AB - A joint-maintenance strategy is proposed for multi-component engineering systems. It achieves the goal of reducing maintenance cost by performing preventive maintenance in groups. Two kinds of preventive maintenance operations are considered, referred to as minor maintenance and major maintenance, renewing each Shop Replaceable Unit (SRU) and the Least Replaceable Unit (LRU) respectively. The focal points of strategy mainly includes three part of costs: (i) cost of maintenance, (ii) cost of working consumption and (iii) cost of economic dependence. A quantum-inspired genetic algorithm (QGA) is proposed to find the optimum major maintenance cycle for LRU and the recurrence rate of minor maintenance for each SRU. The main idea focuses on that significant maintenance cost could be saved by performing preventive maintenance of some SRUs together. To support the optimization of two numerical types at the same time, hybrid coding is adopted in this algorithm to represent feasible solutions. Special evolutionary operations are developed to increase its diversity. It's shown that by performing preventive maintenance of some SRUs together, significant maintenance cost could be saved. A numerical case study is conducted and the experiment results demonstrates that the proposed maintenance strategy and corresponding optimization algorithm is effective in cutting the average maintenance costs.
KW - Joint-maintenance strategy
KW - Maintenance optimization
KW - Multi-component system
KW - Quantum-inspired genetic algorithm
UR - https://www.scopus.com/pages/publications/85110263710
U2 - 10.3850/978-981-14-8593-0_5679-cd
DO - 10.3850/978-981-14-8593-0_5679-cd
M3 - 会议稿件
AN - SCOPUS:85107282822
T3 - 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
SP - 3314
EP - 3320
BT - 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
A2 - Baraldi, Piero
A2 - Di Maio, Francesco
A2 - Zio, Enrico
PB - Research Publishing Services
T2 - 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
Y2 - 1 November 2020 through 5 November 2020
ER -