IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i3p1765-d1039977.html
   My bibliography  Save this article

Two-Stage Multi-Objective Stochastic Model on Patient Transfer and Relief Distribution in Lockdown Area of COVID-19

Author

Listed:
  • Shengjie Long

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Dezhi Zhang

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Shuangyan Li

    (College of Logistics and Transportation, Central South University of Forestry and Technology, Changsha 410004, China)

  • Shuanglin Li

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

Abstract

The outbreak of an epidemic disease may cause a large number of infections and a slightly higher death rate. In response to epidemic disease, both patient transfer and relief distribution are significant to reduce corresponding damage. This study proposes a two-stage multi-objective stochastic model (TMS-PTRD) considering pre-pandemic preparedness measures and post-pandemic relief operations. The proposed model considers the following four objectives: the total number of untreated infected patients, the total transfer time, the overall cost, and the equity distribution of relief supplies. Before an outbreak, the locations of temporary relief distribution centers (TRDCs) and the inventory levels of established TRDCs should be determined. After an outbreak, the locations of temporary hospitals (THs), the locations of designated hospitals (DHs), the transfer plans for patients, and the relief distribution should be determined. To solve the TMS-PTRD model, we address an improved preference-inspired co-evolutionary algorithm named the PICEA-g-AKNN algorithm, which is embedded with a novel similarity distance and three different tailored evolutionary strategies. A real-world case study of Hunan of China and 18 test instances are randomly generated to evaluate the TMS-PTRD model. The finding shows that the PICEA-g-AKNN algorithm is better than some most widely used multi-objective algorithms.

Suggested Citation

  • Shengjie Long & Dezhi Zhang & Shuangyan Li & Shuanglin Li, 2023. "Two-Stage Multi-Objective Stochastic Model on Patient Transfer and Relief Distribution in Lockdown Area of COVID-19," IJERPH, MDPI, vol. 20(3), pages 1-25, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1765-:d:1039977
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/3/1765/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/3/1765/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ruth Banomyong & Paitoon Varadejsatitwong & Richard Oloruntoba, 2019. "A systematic review of humanitarian operations, humanitarian logistics and humanitarian supply chain performance literature 2005 to 2016," Annals of Operations Research, Springer, vol. 283(1), pages 71-86, December.
    2. Ming Liu & Ding Zhang, 2016. "A dynamic logistics model for medical resources allocation in an epidemic control with demand forecast updating," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(6), pages 841-852, June.
    3. Bian Liang & Dapeng Yang & Xinghong Qin & Teresa Tinta, 2019. "A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment," IJERPH, MDPI, vol. 16(20), pages 1-28, October.
    4. Alessandra Cozzolino, 2012. "Humanitarian Logistics," SpringerBriefs in Business, Springer, edition 127, number 978-3-642-30186-5, October.
    5. Eko Setiawan & Jiyin Liu & Alan French, 2019. "Resource location for relief distribution and victim evacuation after a sudden-onset disaster," IISE Transactions, Taylor & Francis Journals, vol. 51(8), pages 830-846, August.
    6. Caunhye, Aakil M. & Nie, Xiaofeng & Pokharel, Shaligram, 2012. "Optimization models in emergency logistics: A literature review," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 4-13.
    7. Ghasemi, Peiman & Khalili-Damghani, Kaveh & Hafezalkotob, Ashkan & Raissi, Sadigh, 2020. "Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    8. Alessandra Cozzolino, 2012. "Humanitarian Logistics and Supply Chain Management," SpringerBriefs in Business, in: Humanitarian Logistics, edition 127, chapter 0, pages 5-16, Springer.
    9. Jian Wang & Danqing Shen & Mingzhu Yu, 2020. "Multiobjective Optimization on Hierarchical Refugee Evacuation and Resource Allocation for Disaster Management," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-18, August.
    10. Büyüktahtakın, İ. Esra & des-Bordes, Emmanuel & Kıbış, Eyyüb Y., 2018. "A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1046-1063.
    11. Ghasemi, Peiman & Khalili-Damghani, Kaveh & Hafezalkotob, Ashkan & Raissi, Sadigh, 2019. "Uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model for earthquake evacuation planning," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 105-132.
    12. Shahparvari, Shahrooz & Abbasi, Babak, 2017. "Robust stochastic vehicle routing and scheduling for bushfire emergency evacuation: An Australian case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 32-49.
    13. Elçi, Özgün & Noyan, Nilay, 2018. "A chance-constrained two-stage stochastic programming model for humanitarian relief network design," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 55-83.
    14. Sheu, Jiuh-Biing & Pan, Cheng, 2014. "A method for designing centralized emergency supply network to respond to large-scale natural disasters," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 284-305.
    15. Ahmadi, Morteza & Seifi, Abbas & Tootooni, Behnam, 2015. "A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 145-163.
    16. Vedat Bayram & Hande Yaman, 2018. "Shelter Location and Evacuation Route Assignment Under Uncertainty: A Benders Decomposition Approach," Transportation Science, INFORMS, vol. 52(2), pages 416-436, March.
    17. Saadatseresht, Mohammad & Mansourian, Ali & Taleai, Mohammad, 2009. "Evacuation planning using multiobjective evolutionary optimization approach," European Journal of Operational Research, Elsevier, vol. 198(1), pages 305-314, October.
    18. Lim, Gino J. & Zangeneh, Shabnam & Reza Baharnemati, M. & Assavapokee, Tiravat, 2012. "A capacitated network flow optimization approach for short notice evacuation planning," European Journal of Operational Research, Elsevier, vol. 223(1), pages 234-245.
    19. Fatemeh Sabouhi & Ali Bozorgi-Amiri & Mohammad Moshref-Javadi & Mehdi Heydari, 2019. "An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: a case study," Annals of Operations Research, Springer, vol. 283(1), pages 643-677, December.
    20. Inmaculada Flores & M. Teresa Ortuño & Gregorio Tirado & Begoña Vitoriano, 2020. "Supported Evacuation for Disaster Relief through Lexicographic Goal Programming," Mathematics, MDPI, vol. 8(4), pages 1-20, April.
    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. Rodríguez-Espíndola, Oscar & Ahmadi, Hossein & Gastélum-Chavira, Diego & Ahumada-Valenzuela, Omar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel, 2023. "Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    2. Dönmez, Zehranaz & Kara, Bahar Y. & Karsu, Özlem & Saldanha-da-Gama, Francisco, 2021. "Humanitarian facility location under uncertainty: Critical review and future prospects," Omega, Elsevier, vol. 102(C).
    3. 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.
    4. Wang, Qingyi & Nie, Xiaofeng, 2022. "A stochastic programming model for emergency supply planning considering transportation network mitigation and traffic congestion," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    5. Ghasemi, Peiman & Khalili-Damghani, Kaveh & Hafezalkotob, Ashkan & Raissi, Sadigh, 2020. "Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    6. Yiping Jiang & Yufei Yuan, 2019. "Emergency Logistics in a Large-Scale Disaster Context: Achievements and Challenges," IJERPH, MDPI, vol. 16(5), pages 1-23, March.
    7. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    8. 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.
    9. Esposito Amideo, A. & Scaparra, M.P. & Kotiadis, K., 2019. "Optimising shelter location and evacuation routing operations: The critical issues," European Journal of Operational Research, Elsevier, vol. 279(2), pages 279-295.
    10. Fatemeh Sabouhi & Ali Bozorgi-Amiri & Mohammad Moshref-Javadi & Mehdi Heydari, 2019. "An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: a case study," Annals of Operations Research, Springer, vol. 283(1), pages 643-677, December.
    11. Sabbaghtorkan, Monir & Batta, Rajan & He, Qing, 2020. "Prepositioning of assets and supplies in disaster operations management: Review and research gap identification," European Journal of Operational Research, Elsevier, vol. 284(1), pages 1-19.
    12. Yin, Yunqiang & Yang, Yongjian & Yu, Yugang & Wang, Dujuan & Cheng, T.C.E., 2023. "Robust vehicle routing with drones under uncertain demands and truck travel times in humanitarian logistics," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    13. Yanyan Wang & Baiqing Sun, 2022. "Multiperiod optimal emergency material allocation considering road network damage and risk under uncertain conditions," Operational Research, Springer, vol. 22(3), pages 2173-2208, July.
    14. Vahdani, Behnam & Veysmoradi, D. & Mousavi, S.M. & Amiri, M., 2022. "Planning for relief distribution, victim evacuation, redistricting and service sharing under uncertainty," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    15. Farahani, Reza Zanjirani & Lotfi, M.M. & Baghaian, Atefe & Ruiz, Rubén & Rezapour, Shabnam, 2020. "Mass casualty management in disaster scene: A systematic review of OR&MS research in humanitarian operations," European Journal of Operational Research, Elsevier, vol. 287(3), pages 787-819.
    16. Özdamar, Linet & Ertem, Mustafa Alp, 2015. "Models, solutions and enabling technologies in humanitarian logistics," European Journal of Operational Research, Elsevier, vol. 244(1), pages 55-65.
    17. Gutjahr, Walter J. & Nolz, Pamela C., 2016. "Multicriteria optimization in humanitarian aid," European Journal of Operational Research, Elsevier, vol. 252(2), pages 351-366.
    18. Bian Liang & Dapeng Yang & Xinghong Qin & Teresa Tinta, 2019. "A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment," IJERPH, MDPI, vol. 16(20), pages 1-28, October.
    19. Aghaie, Sepide & Karimi, Behrooz, 2022. "Location-allocation-routing for emergency shelters based on geographical information system (ArcGIS) by NSGA-II (case study: Earthquake occurrence in Tehran (District-1))," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    20. Maciel M. Queiroz & Dmitry Ivanov & Alexandre Dolgui & Samuel Fosso Wamba, 2022. "Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review," Annals of Operations Research, Springer, vol. 319(1), pages 1159-1196, December.

    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:jijerp:v:20:y:2023:i:3:p:1765-:d:1039977. 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.