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Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints

Author

Listed:
  • Moussa Abderrahim

    (Laboratoire d’Informatique d’Oran (LIO), Université Oran1, Oran 31000, Algeria)

  • Abdelghani Bekrar

    (LAMIH, UMR CNRS 8201, UPHF, 59300 Valenciennes, France)

  • Damien Trentesaux

    (LAMIH, UMR CNRS 8201, UPHF, 59300 Valenciennes, France)

  • Nassima Aissani

    (Laboratoire de l’Ingénierie de la Sécurité Industrielle et du Développement Durable, Université Oran2, Oran 31000, Algeria)

  • Karim Bouamrane

    (Laboratoire d’Informatique d’Oran (LIO), Université Oran1, Oran 31000, Algeria)

Abstract

The industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper, we address the scheduling problem in an AGV-based job-shop manufacturing facility. The considered schedule concerns three strands: jobs affecting machines, product transport tasks’ allocations and AGV fleet battery management. The proposed model supports outcomes expected from Industry 4.0 by increasing productivity through completion time minimization and optimizing energy by managing battery replenishment. Experimental tests were conducted on extended benchmark literature instances to evaluate the efficiency of the proposed approach.

Suggested Citation

  • Moussa Abderrahim & Abdelghani Bekrar & Damien Trentesaux & Nassima Aissani & Karim Bouamrane, 2020. "Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints," Energies, MDPI, vol. 13(18), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4948-:d:416757
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    References listed on IDEAS

    as
    1. Lacomme, Philippe & Larabi, Mohand & Tchernev, Nikolay, 2013. "Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles," International Journal of Production Economics, Elsevier, vol. 143(1), pages 24-34.
    2. Ümit Bilge & Gündüz Ulusoy, 1995. "A Time Window Approach to Simultaneous Scheduling of Machines and Material Handling System in an FMS," Operations Research, INFORMS, vol. 43(6), pages 1058-1070, December.
    3. Olatunde T. Baruwa & Miquel A. Piera, 2016. "A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4773-4792, August.
    4. Maryam Mousavi & Hwa Jen Yap & Siti Nurmaya Musa & Farzad Tahriri & Siti Zawiah Md Dawal, 2017. "Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-24, March.
    5. Vis, Iris F.A., 2006. "Survey of research in the design and control of automated guided vehicle systems," European Journal of Operational Research, Elsevier, vol. 170(3), pages 677-709, May.
    6. A Lusa & C N Potts, 2008. "A variable neighbourhood search algorithm for the constrained task allocation problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(6), pages 812-822, June.
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