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Distributed multi-agent scheduling and control system for robotic flexible assembly cells

Author

Listed:
  • Abderraouf Maoudj

    (Centre de Développement des Technologies Avancées (CDTA)
    Université des sciences et de la technologie Houari-Boumédiène (USTHB))

  • Brahim Bouzouia

    (Centre de Développement des Technologies Avancées (CDTA))

  • Abdelfetah Hentout

    (Centre de Développement des Technologies Avancées (CDTA))

  • Ahmed Kouider

    (Centre de Développement des Technologies Avancées (CDTA))

  • Redouane Toumi

    (Université des sciences et de la technologie Houari-Boumédiène (USTHB))

Abstract

This paper deals with the development of a distributed multi-agent system (DMAS) for scheduling and controlling Robotic Flexible Assembly Cells (RFAC). In the proposed system, an approach for solving one of the most challenging decisional problems in RFAC is proposed and implemented. This problem is related to the products operations scheduling which requires their allocation and sequencing on the robots, while satisfying products and robots constraints under makespan minimization. The proposed DMAS addresses this challenge by using a cooperative approach supported by three kinds of autonomous control agents: supervisory agent, local agents, and remote agents. These agents interact by a negotiation protocol based on common dispatching rules for coordinating their individual decisions, satisfying their local objective and providing an optimized global solution. Moreover, because of the dynamic nature of the assembly systems, it is imperative to consider external disturbances on production scheduling and to solve the related issues. Consequently, DMAS has the ability to respond and manage some dynamic events that may occur in the cells such as unexpected robot breakdown or dynamic products arrivals. Computational results on benchmarks show the effectiveness and the robustness of the proposed system.

Suggested Citation

  • Abderraouf Maoudj & Brahim Bouzouia & Abdelfetah Hentout & Ahmed Kouider & Redouane Toumi, 2019. "Distributed multi-agent scheduling and control system for robotic flexible assembly cells," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1629-1644, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1345-z
    DOI: 10.1007/s10845-017-1345-z
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    References listed on IDEAS

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    1. Abdelhakim AitZai & Brahim Benmedjdoub & Mourad Boudhar, 2016. "Branch-and-bound and PSO algorithms for no-wait job shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 679-688, June.
    2. Vadlamani, Satish & Hosseini, Seyedmohsen, 2014. "A novel heuristic approach for solving aircraft landing problem with single runway," Journal of Air Transport Management, Elsevier, vol. 40(C), pages 144-148.
    3. Ho, Nhu Binh & Tay, Joc Cing & Lai, Edmund M.-K., 2007. "An effective architecture for learning and evolving flexible job-shop schedules," European Journal of Operational Research, Elsevier, vol. 179(2), pages 316-333, June.
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    Cited by:

    1. Xuan Jing & Xifan Yao & Min Liu & Jiajun Zhou, 2024. "Multi-agent reinforcement learning based on graph convolutional network for flexible job shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 75-93, January.
    2. William Derigent & Olivier Cardin & Damien Trentesaux, 2021. "Industry 4.0: contributions of holonic manufacturing control architectures and future challenges," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1797-1818, October.
    3. Olumide Emmanuel Oluyisola & Swapnil Bhalla & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 311-332, January.
    4. Phong B. Dao, 2021. "Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems," Energies, MDPI, vol. 14(2), pages 1-17, January.
    5. Didden, Jeroen B.H.C. & Dang, Quang-Vinh & Adan, Ivo J.B.F., 2024. "Enhancing stability and robustness in online machine shop scheduling: A multi-agent system and negotiation-based approach for handling machine downtime in industry 4.0," European Journal of Operational Research, Elsevier, vol. 316(2), pages 569-583.

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