A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives
| Autor: | Rami Naimi, Maroua Nouiri, Olivier Cardin |
|---|---|
| Přispěvatelé: | Laboratoire des Sciences du Numérique de Nantes (LS2N), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Schedule flexible job shop problem Job shop Computer science Geography Planning and Development Q-learning TJ807-830 Context (language use) 02 engineering and technology Management Monitoring Policy and Law TD194-195 Multi-objective optimization Renewable energy sources [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Reinforcement learning GE1-350 artificial intelligence rescheduling machine failure multi-objective optimization Job shop scheduling Environmental effects of industries and plants Renewable Energy Sustainability and the Environment Energy consumption [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] Industrial engineering Environmental sciences 020201 artificial intelligence & image processing |
| Zdroj: | Sustainability, Vol 13, Iss 13016, p 13016 (2021) Sustainability; Volume 13; Issue 23; Pages: 13016 Sustainability Sustainability, MDPI, 2021, ⟨10.3390/su132313016⟩ |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su132313016⟩ |
| Popis: | International audience; The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system's perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance. |
| Databáze: | OpenAIRE |
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