IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i5p137-d806097.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/5/137/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/5/137/
    Download Restriction: no
    ---><---

    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, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salama, Mohamed R. & Srinivas, Sharan, 2022. "Collaborative truck multi-drone routing and scheduling problem: Package delivery with flexible launch and recovery sites," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    2. Themistoklis Stamadianos & Nikolaos A. Kyriakakis & Magdalene Marinaki & Yannis Marinakis, 2023. "Routing Problems with Electric and Autonomous Vehicles: Review and Potential for Future Research," SN Operations Research Forum, Springer, vol. 4(2), pages 1-34, June.
    3. Pei, Zhi & Dai, Xu & Yuan, Yilun & Du, Rui & Liu, Changchun, 2021. "Managing price and fleet size for courier service with shared drones," Omega, Elsevier, vol. 104(C).
    4. Yichen Lu & Chao Yang & Jun Yang, 2022. "A multi-objective humanitarian pickup and delivery vehicle routing problem with drones," Annals of Operations Research, Springer, vol. 319(1), pages 291-353, December.
    5. Mikhail V. Batsyn & Ekaterina K. Batsyna & Ilya S. Bychkov & Panos M. Pardalos, 2021. "Vehicle assignment in site-dependent vehicle routing problems with split deliveries," Operational Research, Springer, vol. 21(1), pages 399-423, March.
    6. Zhang, Zhe & Song, Xiaoling & Gong, Xue & Yin, Yong & Lev, Benjamin & Zhou, Xiaoyang, 2024. "Coordinated seru scheduling and distribution operation problems with DeJong’s learning effects," European Journal of Operational Research, Elsevier, vol. 313(2), pages 452-464.
    7. Cheng, Chun & Adulyasak, Yossiri & Rousseau, Louis-Martin, 2020. "Drone routing with energy function: Formulation and exact algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 364-387.
    8. Yang Xia & Wenjia Zeng & Xinjie Xing & Yuanzhu Zhan & Kim Hua Tan & Ajay Kumar, 2023. "Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing," Annals of Operations Research, Springer, vol. 327(1), pages 89-127, August.
    9. Polten, Lukas & Emde, Simon, 2022. "Multi-shuttle crane scheduling in automated storage and retrieval systems," European Journal of Operational Research, Elsevier, vol. 302(3), pages 892-908.
    10. Tengkuo Zhu & Stephen D. Boyles & Avinash Unnikrishnan, 2024. "Battery Electric Vehicle Traveling Salesman Problem with Drone," Networks and Spatial Economics, Springer, vol. 24(1), pages 49-97, March.
    11. Herminia I. Calvete & Carmen Galé & José A. Iranzo & Paolo Toth, 2020. "A Partial Allocation Local Search Matheuristic for Solving the School Bus Routing Problem with Bus Stop Selection," Mathematics, MDPI, vol. 8(8), pages 1-20, July.
    12. Ji, Chenlu & Mandania, Rupal & Liu, Jiyin & Liret, Anne, 2022. "Scheduling on-site service deliveries to minimise the risk of missing appointment times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    13. Vincent F. Yu & Shih-Wei Lin & Panca Jodiawan & Yu-Chi Lai, 2023. "Solving the Flying Sidekick Traveling Salesman Problem by a Simulated Annealing Heuristic," Mathematics, MDPI, vol. 11(20), pages 1-21, October.
    14. Skålnes, Jørgen & Ben Ahmed, Mohamed & Hvattum, Lars Magnus & Stålhane, Magnus, 2024. "New benchmark instances for the inventory routing problem," European Journal of Operational Research, Elsevier, vol. 313(3), pages 992-1014.
    15. Li, Zhaojin & Liu, Ya & Yang, Zhen, 2021. "An effective kernel search and dynamic programming hybrid heuristic for a multimodal transportation planning problem with order consolidation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    16. Yin, Yunqiang & Li, Dongwei & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Wang, Sutong, 2023. "A branch-and-price-and-cut algorithm for the truck-based drone delivery routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1125-1144.
    17. Zhao, Lei & Bi, Xinhua & Li, Gendao & Dong, Zhaohui & Xiao, Ni & Zhao, Anni, 2022. "Robust traveling salesman problem with multiple drones: Parcel delivery under uncertain navigation environments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    18. Nasr Al-Hinai & Chefi Triki, 2020. "A two-level evolutionary algorithm for solving the petrol station replenishment problem with periodicity constraints and service choice," Annals of Operations Research, Springer, vol. 286(1), pages 325-350, March.
    19. Li, Feng & Du, Timon C. & Wei, Ying, 2020. "Enhancing supply chain decisions with consumers’ behavioral factors: An illustration of decoy effect," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    20. Tiniç, Gizem Ozbaygin & Karasan, Oya E. & Kara, Bahar Y. & Campbell, James F. & Ozel, Aysu, 2023. "Exact solution approaches for the minimum total cost traveling salesman problem with multiple drones," Transportation Research Part B: Methodological, Elsevier, vol. 168(C), pages 81-123.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:14:y:2022:i:5:p:137-:d:806097. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.