A deep reinforcement learning approach for maintenance planning of multi-component systems with complex structure

Yu, Wenbin

doi:10.1007/s00521-023-08542-9

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2024

2025

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Cited by 4 publications

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Integrating Competencies into Preventive Maintenance Scheduling with Answer Set Optimization

Yli-Jyrä,

Ikävalko,

Janhunen

2024

Lecture Notes in Computer Science

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The maintenance optimization of multi-component machines has been recently formalized as an Answer Set Optimization (ASO) problem based on component selection and grouping of overlapping maintenance intervals. The motivation of the current work is to develop an extension that would integrate resources and availability constraints into this maintenance model. This article outlines an extended ASO model with the primary focus on modeling and optimizing costly maintenance resources, culminating in cost savings facilitated by the progressive development of workforce competence. The model presented in this work extends the cost function of the prior ASO formalization in a modular way with additional cost priorities concerning parallelism, workforce, and expertise. Due to the presented extensions, the complexity of the integrated maintenance model increases compared to the prior formalization.

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