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Simulation of Heuristics for Automated Guided Vehicle Task Sequencing with Resource Sharing and Dynamic Queues

Author

Listed:
  • Jonas F. Leon

    (Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
    Spindox España S.L., 08021 Barcelona, Spain)

  • Mohammad Peyman

    (Research Center on Production Management and Engineering, Universitat Politècnica de València, 03801 Alcoy, Spain)

  • Xabier A. Martin

    (Research Center on Production Management and Engineering, Universitat Politècnica de València, 03801 Alcoy, Spain)

  • Angel A. Juan

    (Research Center on Production Management and Engineering, Universitat Politècnica de València, 03801 Alcoy, Spain)

Abstract

Automated guided vehicles (AGVs) stand out as a paradigmatic application of Industry 4.0, requiring the seamless integration of new concepts and technologies to enhance productivity while reducing labor costs, energy consumption, and emissions. In this context, specific industrial use cases can present a significant technological and scientific challenge. This study was inspired by a real industrial application for which the existing AGV literature did not contain an already well-studied solution. The problem is related to the sequencing of assigned tasks, where the queue formation dynamics and the resource sharing define the scheduling. The combinatorial nature of the problem requires the use of advanced mathematical tools such as heuristics, simulations, or a combination of both. A heuristic procedure was developed that generates candidate task sequences, which are, in turn, evaluated in a discrete-event simulation model developed in Simul8. This combined approach allows high-quality solutions to be generated and realistically evaluated, even graphically, by stakeholders and decision makers. A number of computational experiments were developed to validate the proposed method, which opens up some future lines of research, especially when considering stochastic settings.

Suggested Citation

  • Jonas F. Leon & Mohammad Peyman & Xabier A. Martin & Angel A. Juan, 2024. "Simulation of Heuristics for Automated Guided Vehicle Task Sequencing with Resource Sharing and Dynamic Queues," Mathematics, MDPI, vol. 12(2), pages 1-19, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:271-:d:1318960
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    References listed on IDEAS

    as
    1. Jonas F. Leon & Yuda Li & Mohammad Peyman & Laura Calvet & Angel A. Juan, 2023. "A Discrete-Event Simheuristic for Solving a Realistic Storage Location Assignment Problem," Mathematics, MDPI, vol. 11(7), pages 1-24, March.
    2. Karlijn Fransen & Joost van Eekelen, 2023. "Efficient path planning for automated guided vehicles using A* (Astar) algorithm incorporating turning costs in search heuristic," International Journal of Production Research, Taylor & Francis Journals, vol. 61(3), pages 707-725, February.
    3. Kap Hwan Kim & Jong Wook Bae, 2004. "A Look-Ahead Dispatching Method for Automated Guided Vehicles in Automated Port Container Terminals," Transportation Science, INFORMS, vol. 38(2), pages 224-234, May.
    4. 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.
    5. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
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