IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p743-d729143.html
   My bibliography  Save this article

A Two-Stage Scheduling Model for the Tunnel Collapse under Construction: Rescue and Reconstruction

Author

Listed:
  • Hongjun Cui

    (School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Lijun Liu

    (School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Ying Yang

    (School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Minqing Zhu

    (School of Architecture and Art Design, Hebei University of Technology, Tianjin 300401, China)

Abstract

In the process of transportation system construction, the tunnel is always an indispensable part of the traffic network due to terrain constraints. A collapse of the tunnel under construction may give rise to a potential for significant damage to the traffic network, complicating the road conditions and straining relief services for construction workers. To cope with the variety of vehicle types during the rescue effort, this paper divides them into small, medium, and large sizes, herein correcting the corresponding speed considering six road condition factors on account of the previous research. Given the influence of different special road conditions on the speed of different sized vehicles, a multi-objective model which contains two stages is presented to make decisions for rescue vehicle scheduling. Under the priority of saving human life, the first-stage objective is minimizing the arrival time, while the objective of the second stage includes minimizing the arrival time, unmet demand level, and scheduling cost. To solve the currently proposed model, a non-dominated sorting genetic algorithm II (NSGA-II) with a real number coding method is developed. With a real tunnel example, the acceptability and improvement of the model are examined, and the algorithm’s optimization performance is verified. Moreover, the efficiency of applying real number coding to NSGA-II, the multi-objective gray wolf algorithm (MOGWO), and the traditional genetic algorithm (GA) is compared. The result shows that compared with the other two methods, the NSGA-II algorithm converges faster.

Suggested Citation

  • Hongjun Cui & Lijun Liu & Ying Yang & Minqing Zhu, 2022. "A Two-Stage Scheduling Model for the Tunnel Collapse under Construction: Rescue and Reconstruction," Energies, MDPI, vol. 15(3), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:743-:d:729143
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/743/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/3/743/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Erbeyoğlu, Gökalp & Bilge, Ümit, 2020. "A robust disaster preparedness model for effective and fair disaster response," European Journal of Operational Research, Elsevier, vol. 280(2), pages 479-494.
    2. Alem, Douglas & Clark, Alistair & Moreno, Alfredo, 2016. "Stochastic network models for logistics planning in disaster relief," European Journal of Operational Research, Elsevier, vol. 255(1), pages 187-206.
    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. Chen, Yingzhen & Zhao, Qiuhong & Huang, Kai & Xi, Xunzhuo, 2022. "A Bi-objective optimization model for contract design of humanitarian relief goods procurement considering extreme disasters," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    2. Guo, Penghui & Zhu, Jianjun, 2023. "Capacity reservation for humanitarian relief: A logic-based Benders decomposition method with subgradient cut," European Journal of Operational Research, Elsevier, vol. 311(3), pages 942-970.
    3. Ghasemi, Peiman & Khalili-Damghani, Kaveh, 2021. "A robust simulation-optimization approach for pre-disaster multi-period location–allocation–inventory planning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 69-95.
    4. Seyed Reza Abazari & Fariborz Jolai & Amir Aghsami, 2022. "Designing a humanitarian relief network considering governmental and non-governmental operations under uncertainty," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1430-1452, June.
    5. Hasti Seraji & Reza Tavakkoli-Moghaddam & Sobhan Asian & Harpreet Kaur, 2022. "An integrative location-allocation model for humanitarian logistics with distributive injustice and dissatisfaction under uncertainty," Annals of Operations Research, Springer, vol. 319(1), pages 211-257, December.
    6. Sperling, Martina & Schryen, Guido, 2022. "Decision support for disaster relief: Coordinating spontaneous volunteers," European Journal of Operational Research, Elsevier, vol. 299(2), pages 690-705.
    7. Hu, Shaolong & Han, Chuanfeng & Dong, Zhijie Sasha & Meng, Lingpeng, 2019. "A multi-stage stochastic programming model for relief distribution considering the state of road network," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 64-87.
    8. Zhang, Lingye & Lu, Jing & Yang, Zaili, 2021. "Optimal scheduling of emergency resources for major maritime oil spills considering time-varying demand and transportation networks," European Journal of Operational Research, Elsevier, vol. 293(2), pages 529-546.
    9. Chen, Wanying (Amanda) & De Koster, René B.M. & Gong, Yeming, 2021. "Performance evaluation of automated medicine delivery systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).
    10. Yang, Yongjian & Yin, Yunqiang & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Dhamotharan, Lalitha, 2023. "Distributionally robust multi-period location-allocation with multiple resources and capacity levels in humanitarian logistics," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1042-1062.
    11. Xin Zhang & Gang Zhao & Yingxiu Qi & Botang Li, 2019. "A Robust Fuzzy Optimization Model for Closed-Loop Supply Chain Networks Considering Sustainability," Sustainability, MDPI, vol. 11(20), pages 1-24, October.
    12. Yangjingjing Zhang & Xia Chen & Mengting Gao & Yucheng Dong, 2024. "Maximum Utility Consensus with Inequity Aversion in Social Network Group Decision Making," Group Decision and Negotiation, Springer, vol. 33(5), pages 1115-1142, October.
    13. Sina Abbasi & Ilias Vlachos & Shabnam Rekabi & Mohammad Talooni, 2023. "Designing the Distribution Network of Essential Items in the Critical Conditions of Earthquakes and COVID-19 Simultaneously," Sustainability, MDPI, vol. 15(22), pages 1-23, November.
    14. Raed Al-Husain & Basel Al-Eideh, 2024. "Stochastic Resilience in Operations Management Using Ito Diffusion and Gamma Catastrophe Processes," International Journal of Business Analytics (IJBAN), IGI Global, vol. 11(1), pages 1-16, January.
    15. Abhishek Behl & Pankaj Dutta, 2019. "Humanitarian supply chain management: a thematic literature review and future directions of research," Annals of Operations Research, Springer, vol. 283(1), pages 1001-1044, December.
    16. Zhang, Yuwei & Li, Zhenping & Zhao, Yuwei, 2023. "Multi-mitigation strategies in medical supplies for epidemic outbreaks," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    17. Zhou, Shaorui & Zhang, Hui & Shi, Ning & Xu, Zhou & Wang, Fan, 2020. "A new convergent hybrid learning algorithm for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 283(1), pages 33-46.
    18. Doan, Xuan Vinh & Shaw, Duncan, 2019. "Resource allocation when planning for simultaneous disasters," European Journal of Operational Research, Elsevier, vol. 274(2), pages 687-709.
    19. Claudia Paciarotti & Inna Valiakhmetova, 2021. "Evaluating Disaster Operations Management: An Outcome‐Process Integrated Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 543-562, February.
    20. Dukkanci, Okan & Koberstein, Achim & Kara, Bahar Y., 2023. "Drones for relief logistics under uncertainty after an earthquake," European Journal of Operational Research, Elsevier, vol. 310(1), pages 117-132.

    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:jeners:v:15:y:2022:i:3:p:743-:d:729143. 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.