Policies for the dynamic traveling maintainer problem with alerts
| Autor: | Paulo da Costa, Peter Verleijsdonk, Simon Voorberg, Alp Akcay, Stella Kapodistria, Willem van Jaarsveld, Yingqian Zhang |
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| Přispěvatelé: | Information Systems IE&IS, Stochastic Operations Research, Operations Planning Acc. & Control, EAISI High Tech Systems, EAISI Foundational |
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Deep reinforcement learning Computer Science - Machine Learning Decision process Information Systems and Management General Computer Science Maintenance Degradation process Management Science and Operations Research Industrial and Manufacturing Engineering Machine Learning (cs.LG) Optimization and Control (math.OC) Modeling and Simulation FOS: Mathematics Traveling maintainer problem Mathematics - Optimization and Control |
| Zdroj: | European Journal of Operational Research, 305(3), 1141-1152. Elsevier |
| ISSN: | 0377-2217 |
| DOI: | 10.1016/j.ejor.2022.06.044 |
| Popis: | Downtime of industrial assets such as wind turbines and medical imaging devices comes at a sharp cost. To avoid such downtime costs, companies seek to initiate maintenance just before failure. Unfortunately, this is challenging for the following two reasons: On the one hand, because asset failures are notoriously difficult to predict, even in the presence of real-time monitoring devices which signal early degradation. On the other hand, because the available resources to serve a network of geographically dispersed assets are typically limited. In this paper, we propose a novel model referred to as the dynamic traveling maintainer problem with alerts that incorporates these two challenges and we provide three solution approaches on how to dispatch the limited resources. Namely, we propose: (i) Greedy heuristic approaches that rank assets on urgency, proximity and economic risk; (ii) A novel traveling maintainer heuristic approach that optimizes short-term costs; and (iii) A deep reinforcement learning (DRL) approach that optimizes long-term costs. Each approach has different requirements concerning the available alert information. Experiments with small asset networks show that all methods can approximate the optimal policy when given access to complete condition information. For larger networks, the proposed methods yield competitive policies, with DRL consistently achieving the lowest costs. |
| Databáze: | OpenAIRE |
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