A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

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 inte...

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Bibliographic Details
Published in:Sustainability 2021-12, Vol.13 (23), p.13016
Main Authors: Naimi, Rami, Nouiri, Maroua, Cardin, Olivier
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Breakdowns
Computer Science
Cost control
Decision making
Employment
Energy consumption
Energy efficiency
Genetic algorithms
Heuristic
Integer programming
Job shops
Learning algorithms
Machine learning
Manufacturing
Methods
Operations Research
Optimization
Process controls
Productivity
Rescheduling
Researchers
Scheduling
Sustainability
Systems stability
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su132313016