Energy-oriented opportunistic maintenance optimization of continuous process manufacturing systems with two types of stochastic durations

• Published in: Reliability engineering & system safety Vol. 237; p. 109385
• Main Authors: Chen, Zhaoxiang, Chen, Zhen, Zhou, Di, Pan, Ershun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2023
• Subjects: Continuous process manufacturing systems; Energy; Genetic algorithm; Opportunistic maintenance; Stochastic durations; Energy; Genetic algorithm; Stochastic durations; Continuous process manufacturing systems; Opportunistic maintenance
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2023.109385

•We study energy-oriented opportunistic maintenance for CPMS.•The "opportunities" constrained by production characteristics are utilized for maintenance.•The randomness of production batch duration and maintenance duration is analyzed.•SFMN is built to describe dynamic production processes under uncertainties.•A tailored GAMES is developed with an evolutionary mechanism guided by statistics. For continuous process manufacturing systems (CPMSs) where the production process cannot be stopped, the “opportunities” for maintenance can only occur within the specified time intervals between two production batches. Moreover, opportunistic maintenance of CPMSs is not only to reduce downtime losses and maintenance costs, but also to improve productivity and energy efficiency. However, existing studies ignored stochastic uncertainties of production batch duration and maintenance duration, which can lead to overestimation of total benefit and reliability, and increase the risk of accidents. Therefore, an energy-oriented opportunistic maintenance (EOM) strategy for CPMSs with stochastic durations is proposed. The stochastic opportunity time window (SOTW) is introduced to characterize the uncertain “opportunity” of maintenance caused by the above-mentioned stochastic uncertainties. And, a stochastic flow manufacturing network (SFMN) is established to evaluate machine reliability and energy consumption under the internal and external uncertainties. Moreover, the optimization objective of EOM that takes into account energy, production and maintenance simultaneously is to maximize the expected system benefits by selecting appropriate maintenance actions during the SOTWs. Then, a genetic algorithm with multiple evolution strategies (GAMES) is developed to address the optimization problem. Finally, a case study is provided to verify the effectiveness of the proposed method.