4.7 Article

Joint optimization of fleet-level sequential selective maintenance and repairpersons assignment for multi-state manufacturing systems

Journal

COMPUTERS & INDUSTRIAL ENGINEERING
Volume 182, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2023.109411

Keywords

Multi-state manufacturing system; Selective maintenance; Repairpersons assignment; Flow dependency; Uncertain maintenance duration; Genetic algorithm

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This paper proposes a novel joint optimization model of fleet-level sequential selective maintenance and repairpersons assignment under flow dependency and uncertain maintenance duration. The model involves identifying a subset of maintenance actions, assigning them to repairpersons, and planning their execution sequence. It also incorporates the degradation model of multi-state machines under flow dependency and considers the interaction among repairpersons, flow dependency, and uncertain maintenance duration. The objective is to maximize the total profit under predetermined system reliability thresholds.
Due to limited maintenance resources, the joint optimization of fleet-level selective maintenance and repair persons assignment aims to determine the optimal subset of maintenance actions for each repairperson to ensure that manufacturing systems in the fleet complete subsequent missions. However, related studies ignore the uncertain maintenance duration and flow dependency (a stochastic dependency caused by dynamic material flow) resulting in overestimation of system reliability and total profit. Therefore, this paper proposes a novel joint optimization model of fleet-level sequential selective maintenance and repairpersons assignment under flow dependency and uncertain maintenance duration. The model requires identifying a subset of maintenance actions, assigning the selected maintenance actions to repairpersons, and planning sequence of maintenance actions executed by each repairperson. Moreover, the degradation model of multi-state machines under flow dependency is established by analyzing dynamic characteristics of production processes. Further, the reliability of multi-state manufacturing systems considering the interaction among repairpersons, flow dependency, and uncertain maintenance duration is incorporated into the optimization model. The objective is to maximize the total profit under predetermined system reliability thresholds. Then, the optimization problem is solved by a tailored genetic algorithm with segment-based evolution strategy. Finally, numerical examples are given to verify the effectiveness of the proposed method.

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