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Co-Simulation of Multiple Vehicle Routing Problem Models

Author

Listed:
  • Sana Sahar Guia

    (LIAP Laboratory, University of El Oued, El Oued 39000, Algeria)

  • Abdelkader Laouid

    (LIAP Laboratory, University of El Oued, El Oued 39000, Algeria)

  • Mohammad Hammoudeh

    (Information & Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Ahcène Bounceur

    (Lab-STICC UMR CNRS, University of Western Brittany UBO, 6285 Brest, France)

  • Mai Alfawair

    (Faculty of Information Technology, AlBalqa Applied University, Amman 11134, Jordan)

  • Amna Eleyan

    (School of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK)

Abstract

Complex systems are often designed in a decentralized and open way so that they can operate on heterogeneous entities that communicate with each other. Numerous studies consider the process of components simulation in a complex system as a proven approach to realistically predict the behavior of a complex system or to effectively manage its complexity. The simulation of different complex system components can be coupled via co-simulation to reproduce the behavior emerging from their interaction. On the other hand, multi-agent simulations have been largely implemented in complex system modeling and simulation. Each multi-agent simulator’s role is to solve one of the VRP objectives. These simulators interact within a co-simulation platform called MECSYCO, to ensure the integration of the various proposed VRP models. This paper presents the Vehicle Routing Problem (VRP) simulation results in several aspects, where the main goal is to satisfy several client demands. The experiments show the performance of the proposed VRP multi-model and carry out its improvement in terms of computational complexity.

Suggested Citation

  • Sana Sahar Guia & Abdelkader Laouid & Mohammad Hammoudeh & Ahcène Bounceur & Mai Alfawair & Amna Eleyan, 2022. "Co-Simulation of Multiple Vehicle Routing Problem Models," Future Internet, MDPI, vol. 14(5), pages 1-16, April.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:5:p:137-:d:806097
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    References listed on IDEAS

    as
    1. Vidal, Thibaut & Laporte, Gilbert & Matl, Piotr, 2020. "A concise guide to existing and emerging vehicle routing problem variants," European Journal of Operational Research, Elsevier, vol. 286(2), pages 401-416.
    2. Uchoa, Eduardo & Pecin, Diego & Pessoa, Artur & Poggi, Marcus & Vidal, Thibaut & Subramanian, Anand, 2017. "New benchmark instances for the Capacitated Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 257(3), pages 845-858.
    3. Martin, Simon & Ouelhadj, Djamila & Beullens, Patrick & Ozcan, Ender & Juan, Angel A. & Burke, Edmund K., 2016. "A multi-agent based cooperative approach to scheduling and routing," European Journal of Operational Research, Elsevier, vol. 254(1), pages 169-178.
    4. Wang, Zheng & Sheu, Jiuh-Biing, 2019. "Vehicle routing problem with drones," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 350-364.
    5. Tim Gooding, 2019. "Economics for a Fairer Society," Springer Books, Springer, number 978-3-030-17020-2, February.
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