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

Rami Naimi, Maroua Nouiri and Olivier Cardin
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Rami Naimi: LS2N UMR CNRS 6004, IUT de Nantes, Nantes University, 2 Avenue du Pr. J. Rouxel, 44470 Carquefou, France
Maroua Nouiri: LS2N UMR CNRS 6004, IUT de Nantes, Nantes University, 2 Avenue du Pr. J. Rouxel, 44470 Carquefou, France
Olivier Cardin: LS2N UMR CNRS 6004, IUT de Nantes, Nantes University, 2 Avenue du Pr. J. Rouxel, 44470 Carquefou, France

Sustainability, 2021, vol. 13, issue 23, 1-36

Abstract: 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.

Keywords: flexible job shop problem; artificial intelligence; rescheduling; Q-learning; machine failure; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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