强化学习
信息物理系统
调度(生产过程)
背景(考古学)
计算机科学
可靠性工程
过程(计算)
层次分析法
可靠性(半导体)
工业工程
工程类
运筹学
人工智能
运营管理
量子力学
生物
操作系统
古生物学
功率(物理)
物理
作者
Fatima Ezzahra Achamrah,Ali Attajer
标识
DOI:10.1080/00207543.2023.2240433
摘要
Unlike mass production manufacturing systems, where configurations are rarely changed after the initial design, reconfigurable cyber-physical systems (RCPMS) self-change their structures throughout missions and thus self-adjust production in response to demand requirements. Accordingly, such a paradigm requires enhancing selective maintenance strategy to optimise scheduling maintenance actions, selecting configuration layouts for capacity and product family changes, and achieving maintenance cost reduction and reliability maximisation. This paper is the first to propose a robust model for a selective maintenance problem with imperfect repairs in the RCPMS context. The model also integrates uncertainties originating from the imperfect observations of components' health status. The model's objectives are to maximise the expected reliability and minimise the variance and maintenance cost under maintenance resource constraints. Moreover, we propose a new deep reinforcement learning framework for solving the resulting multi-objective and combinatorial optimisation problem. In addition, we use decision values to enhance the scalarisation process by permitting the priorities of specific objectives to be adjusted after the learning process. Furthermore, we employ Analytical Hierarchy Process to adjust the static priorities with respect to the objective functions and the actual learning context. Finally, broad experiments are conducted to highlight the performance of the proposed model and resolution framework.
科研通智能强力驱动
Strongly Powered by AbleSci AI