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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rami Naimi & Maroua Nouiri & Olivier Cardin, 2021. "A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives," Sustainability, MDPI, vol. 13(23), pages 1-36, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13016-:d:686945
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    References listed on IDEAS

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    1. Chuang Wang & Pingyu Jiang, 2018. "Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1485-1500, October.
    2. B. Mihoubi & B. Bouzouia & M. Gaham, 2021. "Reactive scheduling approach for solving a realistic flexible job shop scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 59(19), pages 5790-5808, October.
    3. Sylverin Kemmoe & Damien Lamy & Nikolay Tchernev, 2017. "Job-shop like manufacturing system with variable power threshold and operations with power requirements," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6011-6032, October.
    4. Maroua Nouiri & Abdelghani Bekrar & Damien Trentesaux, 2020. "An energy-efficient scheduling and rescheduling method for production and logistics systems†," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3263-3283, June.
    5. Yu-Fang Wang, 2020. "Adaptive job shop scheduling strategy based on weighted Q-learning algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 417-432, February.
    6. Liu, Ying & Dong, Haibo & Lohse, Niels & Petrovic, Sanja, 2016. "A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance," International Journal of Production Economics, Elsevier, vol. 179(C), pages 259-272.
    7. Masmoudi, Oussama & Delorme, Xavier & Gianessi, Paolo, 2019. "Job-shop scheduling problem with energy consideration," International Journal of Production Economics, Elsevier, vol. 216(C), pages 12-22.
    8. Silviu Raileanu & Florin Anton & Alexandru Iatan & Theodor Borangiu & Silvia Anton & Octavian Morariu, 2017. "Resource scheduling based on energy consumption for sustainable manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(7), pages 1519-1530, October.
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