A Semi Adaptive Large Neighborhood Search for the Maintenance Scheduling and Routing Problem
| Autor: | Abdeslam Kadrani, Rym Nesrine Guibadj, Rachid Benmansour, Cyril Fonlupt, Lamiaa Dahite |
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| Přispěvatelé: | Laboratoire d'Informatique Signal et Image de la Côte d'Opale (LISIC), Université du Littoral Côte d'Opale (ULCO), Institut National de Statistique et d’Economie Appliquée [Rabat] (INSEA) |
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
021103 operations research Learning automata Computer science 0211 other engineering and technologies 02 engineering and technology Diversification (marketing strategy) Solver Maintenance engineering Scheduling (computing) 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] 020201 artificial intelligence & image processing Routing (electronic design automation) Heuristics Metaheuristic ComputingMilieux_MISCELLANEOUS |
| Zdroj: | 2021 7th International Conference on Optimization and Applications (ICOA) 2021 7th International Conference on Optimization and Applications (ICOA), May 2021, Wolfenbüttel, Germany. pp.1-6, ⟨10.1109/ICOA51614.2021.9442627⟩ |
| DOI: | 10.1109/icoa51614.2021.9442627 |
| Popis: | This work presents a Semi Adaptive Large Neighborhood Search (SALNS) for the Maintenance Scheduling and Routing Problem. A new removal method based on the behavior of risk in maintenance is proposed. It is combined with several destroy and repair operators. A semi adaptive mechanism that ensures effective mix between diversification and learning is proposed. We conduct a comparative analysis with the solver and with adapted algorithms schemes from the literature: classical ALNS and ALNS with learning automata (LA-ALNS). All the algorithms consider the same choices related to problem’s specifications and use the same local search procedure. The proposed metaheuristic chooses the most suitable heuristics while alternating between learning and diversification to obtain high quality solutions. |
| Databáze: | OpenAIRE |
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