Dynamic and Adaptive Grouping Maintenance Strategies: New Scalable Optimization Algorithms
| Autor: | Makhlouf Hadji, Selma Khebbache, Laurent Bouillaut, Maria Hanini |
|---|---|
| Přispěvatelé: | IRT SystemX (IRT SystemX) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization 021103 operations research Series (mathematics) Optimization algorithm Adaptive optimization Computer science 0211 other engineering and technologies Constrained clustering Complex system Particle swarm optimization 02 engineering and technology [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] 020901 industrial engineering & automation Scalability [MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO] Safety Risk Reliability and Quality ComputingMilieux_MISCELLANEOUS |
| Zdroj: | Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, SAGE Publications, 2021 |
| ISSN: | 1748-006X 1748-0078 |
| Popis: | This paper focuses on new efficient and adaptive optimization algorithms to cope with the maintenance grouping problem for series, parallel, and complex systems. We propose a Particle Swarm Optimization approach to cope with small and medium problem sizes, and that will be used to benchmark existing heuristic solutions such as Genetic Algorithms. To address scalability and adaptability issues, we propose a new dynamic optimization algorithm based on a clustering technique. This clustering-based solution is formulated using an Integer Linear Programing approach to guarantee the convergence to global optimal solutions of the considered problem. We show the performance of the proposed approaches with a clear advantage to the clustering-based algorithm that we recommend for large industrial systems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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