IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v9y2018i3d10.1007_s13198-017-0648-y.html
   My bibliography  Save this article

Multi objective optimization of railway emergency rescue resource allocation and decision

Author

Listed:
  • Zhaoping Tang

    (East China Jiaotong University)

  • Jianping Sun

    (East China Jiaotong University)

Abstract

Based on the characteristics and requirements of railway emergency resources dispatch, the paper established two optimal models respectively, the single objective model is aiming at minimizing the time of emergency resource dispatch, the multi objective model is for the sake of minimizing the time of emergency resource dispatch and the number of emergency rescue base, and the paper used voting analytic hierarchy process of operations research to solve the model. The study shows that the multi objective optimization model can reduce the number of emergency rescue base and the cost of rescue while it can meet the demand of the shortest allocation time. By using Matlab software, the decision-making process of emergency resource optimize dispatch was designed, making the emergency decision fast and scientific, meanwhile, the paper made an empirical analysis on one railway bureau. The research results reduce the rescue time and cost of the railway accident, and provide reference for implement the rapidness response to make decision on emergency resources allocation, scientific resource allocation decisions.

Suggested Citation

  • Zhaoping Tang & Jianping Sun, 2018. "Multi objective optimization of railway emergency rescue resource allocation and decision," 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. 9(3), pages 696-702, June.
  • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:3:d:10.1007_s13198-017-0648-y
    DOI: 10.1007/s13198-017-0648-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-017-0648-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13198-017-0648-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. G Barbarosoǧlu & Y Arda, 2004. "A two-stage stochastic programming framework for transportation planning in disaster response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(1), pages 43-53, January.
    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. Huizhu Wang & Jianqin Zhou, 2023. "Location of Railway Emergency Rescue Spots Based on a Near-Full Covering Problem: From a Perspective of Diverse Scenarios," Sustainability, MDPI, vol. 15(8), pages 1-16, April.

    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. Fuyu Wang & Xuefei Ge & Yan Li & Jingjing Zheng & Weichen Zheng, 2023. "Optimising the Distribution of Multi-Cycle Emergency Supplies after a Disaster," Sustainability, MDPI, vol. 15(2), pages 1-26, January.
    2. Wang, Haijun & Du, Lijing & Ma, Shihua, 2014. "Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 160-179.
    3. Cailin Wang & Jidong Wu & Xin He & Mengqi Ye & Wenhui Liu & Rumei Tang, 2018. "Emerging Trends and New Developments in Disaster Research after the 2008 Wenchuan Earthquake," IJERPH, MDPI, vol. 16(1), pages 1-19, December.
    4. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    5. A. Anaya-Arenas & J. Renaud & A. Ruiz, 2014. "Relief distribution networks: a systematic review," Annals of Operations Research, Springer, vol. 223(1), pages 53-79, December.
    6. Dilsu Binnaz Ozkapici & Mustafa Alp Ertem & Haluk Aygüneş, 2016. "Intermodal humanitarian logistics model based on maritime transportation in Istanbul," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 345-364, August.
    7. Lu, Chung-Cheng & Ying, Kuo-Ching & Chen, Hui-Ju, 2016. "Real-time relief distribution in the aftermath of disasters – A rolling horizon approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 1-20.
    8. Wilson, Duncan T. & Hawe, Glenn I. & Coates, Graham & Crouch, Roger S., 2013. "A multi-objective combinatorial model of casualty processing in major incident response," European Journal of Operational Research, Elsevier, vol. 230(3), pages 643-655.
    9. Rafiei, Rezvan & Huang, Kai & Verma, Manish, 2022. "Cash versus in-kind transfer programs in humanitarian operations: An optimization program and a case study," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    10. P. Daniel Wright & Matthew J. Liberatore & Robert L. Nydick, 2006. "A Survey of Operations Research Models and Applications in Homeland Security," Interfaces, INFORMS, vol. 36(6), pages 514-529, December.
    11. Yagci Sokat, Kezban & Dolinskaya, Irina S. & Smilowitz, Karen & Bank, Ryan, 2018. "Incomplete information imputation in limited data environments with application to disaster response," European Journal of Operational Research, Elsevier, vol. 269(2), pages 466-485.
    12. Pengxia Zhao & Tie Li & Biao Wang & Ming Li & Yu Wang & Xiahui Guo & Yue Yu, 2022. "The Scenario Construction and Evolution Method of Casualties in Liquid Ammonia Leakage Based on Bayesian Network," IJERPH, MDPI, vol. 19(24), pages 1-22, December.
    13. Eghbal Akhlaghi, Vahid & Campbell, Ann Melissa, 2022. "The two-echelon island fuel distribution problem," European Journal of Operational Research, Elsevier, vol. 302(3), pages 999-1017.
    14. 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.
    15. Rawls, Carmen G. & Turnquist, Mark A., 2012. "Pre-positioning and dynamic delivery planning for short-term response following a natural disaster," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 46-54.
    16. Shuwan Zhu & Wenjuan Fan & Xueping Li & Shanlin Yang, 2023. "Ambulance dispatching and operating room scheduling considering reusable resources in mass-casualty incidents," Operational Research, Springer, vol. 23(2), pages 1-37, June.
    17. Li, Lingfeng & Jin, Mingzhou & Zhang, Li, 2011. "Sheltering network planning and management with a case in the Gulf Coast region," International Journal of Production Economics, Elsevier, vol. 131(2), pages 431-440, June.
    18. de la Torre, Luis E. & Dolinskaya, Irina S. & Smilowitz, Karen R., 2012. "Disaster relief routing: Integrating research and practice," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 88-97.
    19. Seyed Babak Ebrahimi & Ehsan Bagheri, 2022. "A multi-objective formulation for the closed-loop plastic supply chain under uncertainty," Operational Research, Springer, vol. 22(5), pages 4725-4768, November.
    20. Fei, Xin & Gülpınar, Nalân & Branke, Jürgen, 2019. "Efficient solution selection for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 277(3), pages 918-929.

    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:spr:ijsaem:v:9:y:2018:i:3:d:10.1007_s13198-017-0648-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.