Implementation of a cost optimization algorithm in a context of distributed maintenance
| Autor: | Zineb Simeu-Abazi, Eric Gascard |
|---|---|
| Přispěvatelé: | Gestion et Conduite des Systèmes de Production (G-SCOP_GCSP), Laboratoire des sciences pour la conception, l'optimisation et la production (G-SCOP), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
021110 strategic
defence & security studies Genetic Algorithm 021103 operations research Cost evaluation Computer science Scheduling 0211 other engineering and technologies Availability 02 engineering and technology [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering Cost optimization Scheduling (computing) [SPI.AUTO]Engineering Sciences [physics]/Automatic Distributed maintenance Spare part Simulated annealing Simulated Annealing Algorithm |
| Zdroj: | ICCAD 2020-4th International Conference on Control, Automation and Diagnosis ICCAD 2020-4th International Conference on Control, Automation and Diagnosis, Oct 2020, Paris, France. ⟨10.1109/ICCAD49821.2020.9260507⟩ HAL |
| DOI: | 10.1109/ICCAD49821.2020.9260507⟩ |
| Popis: | International audience; This paper concerns the application of the maintenance task scheduling approach in a distributed context. We are particularly interested in the case where there is, on the one hand, a central maintenance workshop (CMW) in which therepair cycles are carried out and, on the other hand, a mobile maintenance workshop (MMW) for repair by replacement at several sites according to a predefined schedule. The maintenance costs are then partly linked to the costs of spare parts, travel, repair or replacement times. A maintenance cost optimization methodology based on the use of meta-heuristics is proposed in this article. A comparative study was carried out using two optimization algorithms: the genetic algorithm and the simulated annealing. The algorithms were applied to a company in the Rhône-Alpes region. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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