IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i13p2184-d845716.html
   My bibliography  Save this article

An Improved Intelligent Auction Mechanism for Emergency Material Delivery

Author

Listed:
  • Jie Zhang

    (School of Systems Engineering, National University of Defense Technology, Changsha 410000, China)

  • Yifan Zhu

    (School of Systems Engineering, National University of Defense Technology, Changsha 410000, China)

  • Tao Wang

    (School of Systems Engineering, National University of Defense Technology, Changsha 410000, China)

  • Weiping Wang

    (School of Systems Engineering, National University of Defense Technology, Changsha 410000, China)

  • Rui Wang

    (School of Systems Engineering, National University of Defense Technology, Changsha 410000, China)

  • Xiaobo Li

    (School of Systems Engineering, National University of Defense Technology, Changsha 410000, China)

Abstract

Emergency material delivery is vital to disaster emergency rescue. Herein, the framework of the emergency material delivery system (EMDS) with the unmanned aerial vehicle (UAV) as the vehicle is proposed, and the problem is modeled into a multi-trip time-dependent dynamic vehicle routing problem with split-delivery (MTTDDVRP-SD) in combination with the rescue reality, which provides decision support for planning disaster relief material. Due to the universality of dynamic interference in the process of material delivery, an optimization algorithm based on the traditional intelligent auction mechanism is proposed to avoid system performance degradation or even collapse. The algorithm adds pre-authorization and sequential auction mechanisms to the traditional auction mechanism, where the pre-authorization mechanism improves the capability performance of the system when there is no interference during the rescue process and the sequential auction mechanism improves the resilience performance of the system when it faces interferences. Finally, considering three types of interference comprehensively, which includes new task generations , task unexpected changes and UAV’s number decreases , the proposed algorithm is compared with DTAP (DTA based on sequential single item auctions) and CBBA-PR (consensus-based bundle algorithms-partial replanning) algorithms under different dynamic interference intensity scenarios for simulation experimental from two perspectives of the capability performance and resilience performance. The results of Friedman’s test with 99% confidence interval indicate that the proposed algorithm can effectively improve the capability performance and resilience performance of EMDS.

Suggested Citation

  • Jie Zhang & Yifan Zhu & Tao Wang & Weiping Wang & Rui Wang & Xiaobo Li, 2022. "An Improved Intelligent Auction Mechanism for Emergency Material Delivery," Mathematics, MDPI, vol. 10(13), pages 1-30, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2184-:d:845716
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/13/2184/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/13/2184/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Patterson, Mary D & Wears, Robert L, 2015. "Resilience and precarious success," Reliability Engineering and System Safety, Elsevier, vol. 141(C), pages 45-53.
    2. Diego Cattaruzza & Nabil Absi & Dominique Feillet, 2018. "Vehicle routing problems with multiple trips," Annals of Operations Research, Springer, vol. 271(1), pages 127-159, December.
    3. Sadrani, Mohammad & Tirachini, Alejandro & Antoniou, Constantinos, 2022. "Vehicle dispatching plan for minimizing passenger waiting time in a corridor with buses of different sizes: Model formulation and solution approaches," European Journal of Operational Research, Elsevier, vol. 299(1), pages 263-282.
    4. Gaofeng Guan & Zijun Lin & Yu Gong & Zhijuan Jiang, 2020. "Modeling and Simulation of Collaborative Dispatching of Disaster Relief Materials Based on Urgency," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, August.
    5. Francis, Royce & Bekera, Behailu, 2014. "A metric and frameworks for resilience analysis of engineered and infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 90-103.
    6. Casazza, Marco & Ceselli, Alberto & Wolfler Calvo, Roberto, 2021. "A route decomposition approach for the single commodity Split Pickup and Split Delivery Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 289(3), pages 897-911.
    7. Ju Myung Song & Weiwei Chen & Lei Lei, 2018. "Supply chain flexibility and operations optimisation under demand uncertainty: a case in disaster relief," International Journal of Production Research, Taylor & Francis Journals, vol. 56(10), pages 3699-3713, May.
    8. Felipe Campelo & Elizabeth F. Wanner, 2020. "Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances," Journal of Heuristics, Springer, vol. 26(6), pages 851-883, December.
    9. Gama Dessavre, Dante & Ramirez-Marquez, Jose E. & Barker, Kash, 2016. "Multidimensional approach to complex system resilience analysis," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 34-43.
    10. R. Montemanni & L. M. Gambardella & A. E. Rizzoli & A. V. Donati, 2005. "Ant Colony System for a Dynamic Vehicle Routing Problem," Journal of Combinatorial Optimization, Springer, vol. 10(4), pages 327-343, December.
    11. Aristide Mingozzi & Roberto Roberti & Paolo Toth, 2013. "An Exact Algorithm for the Multitrip Vehicle Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 193-207, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li Ma & Minghan Xin & Yi-Jia Wang & Yanjiao Zhang, 2022. "Dynamic Scheduling Strategy for Shared Agricultural Machinery for On-Demand Farming Services," Mathematics, MDPI, vol. 10(21), pages 1-22, October.

    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. Poulin, Craig & Kane, Michael B., 2021. "Infrastructure resilience curves: Performance measures and summary metrics," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Zhao, S. & Liu, X. & Zhuo, Y., 2017. "Hybrid Hidden Markov Models for resilience metrics in a dynamic infrastructure system," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 84-97.
    3. Cai, Baoping & Zhang, Yanping & Wang, Haifeng & Liu, Yonghong & Ji, Renjie & Gao, Chuntan & Kong, Xiangdi & Liu, Jing, 2021. "Resilience evaluation methodology of engineering systems with dynamic-Bayesian-network-based degradation and maintenance," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    4. Cheng, Yao & Elsayed, E.A. & Chen, Xi, 2021. "Random Multi Hazard Resilience Modeling of Engineered Systems and Critical Infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    5. Côté, Jean-François & Alves de Queiroz, Thiago & Gallesi, Francesco & Iori, Manuel, 2023. "A branch-and-regret algorithm for the same-day delivery problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    6. Das, Laya & Munikoti, Sai & Natarajan, Balasubramaniam & Srinivasan, Babji, 2020. "Measuring smart grid resilience: Methods, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    7. Cai, Baoping & Xie, Min & Liu, Yonghong & Liu, Yiliu & Feng, Qiang, 2018. "Availability-based engineering resilience metric and its corresponding evaluation methodology," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 216-224.
    8. Wang, Jing & Zuo, Wangda & Rhode-Barbarigos, Landolf & Lu, Xing & Wang, Jianhui & Lin, Yanling, 2019. "Literature review on modeling and simulation of energy infrastructures from a resilience perspective," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 360-373.
    9. Cassottana, Beatrice & Shen, Lijuan & Tang, Loon Ching, 2019. "Modeling the recovery process: A key dimension of resilience," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    10. Tran, Huy T. & Balchanos, Michael & Domerçant, Jean Charles & Mavris, Dimitri N., 2017. "A framework for the quantitative assessment of performance-based system resilience," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 73-84.
    11. Adel Mottahedi & Farhang Sereshki & Mohammad Ataei & Ali Nouri Qarahasanlou & Abbas Barabadi, 2021. "The Resilience of Critical Infrastructure Systems: A Systematic Literature Review," Energies, MDPI, vol. 14(6), pages 1-32, March.
    12. Umunnakwe, A. & Huang, H. & Oikonomou, K. & Davis, K.R., 2021. "Quantitative analysis of power systems resilience: Standardization, categorizations, and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    13. Jie Zhang & Yifan Zhu & Xiaobo Li & Mengjun Ming & Weiping Wang & Tao Wang, 2022. "Multi-Trip Time-Dependent Vehicle Routing Problem with Split Delivery," Mathematics, MDPI, vol. 10(19), pages 1-24, September.
    14. Hu, Jinqiu & Khan, Faisal & Zhang, Laibin, 2021. "Dynamic resilience assessment of the Marine LNG offloading system," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    15. Jumbo, Olga & Moghaddass, Ramin, 2022. "Resource optimization and image processing for vegetation management programs in power distribution networks," Applied Energy, Elsevier, vol. 319(C).
    16. Shen, Lijuan & Cassottana, Beatrice & Tang, Loon Ching, 2018. "Statistical trend tests for resilience of power systems," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 138-147.
    17. Trucco, Paolo & Petrenj, Boris, 2023. "Characterisation of resilience metrics in full-scale applications to interdependent infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    18. Lars M. Hvattum & Arne Løkketangen & Gilbert Laporte, 2006. "Solving a Dynamic and Stochastic Vehicle Routing Problem with a Sample Scenario Hedging Heuristic," Transportation Science, INFORMS, vol. 40(4), pages 421-438, November.
    19. Nicolas Rincon-Garcia & Ben J. Waterson & Tom J. Cherrett, 2018. "Requirements from vehicle routing software: perspectives from literature, developers and the freight industry," Transport Reviews, Taylor & Francis Journals, vol. 38(1), pages 117-138, January.
    20. Fauzan Hanif Jufri & Jun-Sung Kim & Jaesung Jung, 2017. "Analysis of Determinants of the Impact and the Grid Capability to Evaluate and Improve Grid Resilience from Extreme Weather Event," Energies, MDPI, vol. 10(11), pages 1-17, November.

    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:jmathe:v:10:y:2022:i:13:p:2184-:d:845716. 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.