IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p4882-d1092398.html
   My bibliography  Save this article

Scenario-Based Multi-Objective Location-Routing Model for Pre-Disaster Planning: A Philippine Case Study

Author

Listed:
  • Maria Rossana D. de Veluz

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    School of Graduate Studies, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    College of Engineering, Southern Luzon State University, Lucban 4328, Philippines)

  • Anak Agung Ngurah Perwira Redi

    (Industrial Engineering Department, Faculty of Engineering and Technology, Sampoerna University, Jakarta 12780, Indonesia)

  • Renato R. Maaliw

    (College of Engineering, Southern Luzon State University, Lucban 4328, Philippines)

  • Satria Fadil Persada

    (BINUS Business School Undergraduate Program, Entrepreneurship Department, Bina Nusantara University, Malang 65154, Indonesia)

  • Yogi Tri Prasetyo

    (International Bachelor Program in Engineering, Yuan Ze University, 135 Yuan-Tung Roa-Chung-Li, Taoyuan City 32003, Taiwan
    Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Roa-Chung-Li, Taoyuan City 32003, Taiwan)

  • Michael Nayat Young

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

Abstract

The demand for humanitarian supply chains grows daily as the incidence of calamities rises. Typhoons cause thousands of casualties each year. As a result, policymakers and governmental authorities must develop effective readiness and response measures as part of pre-disaster plans. This paper proposed a stochastic model for multi-objective location-routing for creating a humanitarian network for pre-disaster response. The model aims to minimize the overall costs of the network’s setup, the time required to travel through it, and the number of vehicles necessary for transferring affected individuals to evacuation centers. The model concentrates on pre-disaster scenarios in uncertainty. The provided model was implemented in an actual scenario in one of the Philippines’ provinces and solved using Multi-Objective Particle Swarm Optimization (MOPSO), which is also contrasted with Multi-Objective Simulated Annealing (MOSA) and the ε-constraint approach. According to empirical findings, the model can be used to identify distribution hubs and evacuation centers and choose the best routes in unexpected and actual disaster scenarios. Given that the ideal number, location, and capacity of DCs and ECs are known in advance, government decision-makers can solve any potential shortages and problems during the disaster.

Suggested Citation

  • Maria Rossana D. de Veluz & Anak Agung Ngurah Perwira Redi & Renato R. Maaliw & Satria Fadil Persada & Yogi Tri Prasetyo & Michael Nayat Young, 2023. "Scenario-Based Multi-Objective Location-Routing Model for Pre-Disaster Planning: A Philippine Case Study," Sustainability, MDPI, vol. 15(6), pages 1-33, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4882-:d:1092398
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/4882/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/4882/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jamie Dekle & Mariel S. Lavieri & Erica Martin & Hülya Emir-Farinas & Richard L. Francis, 2005. "A Florida County Locates Disaster Recovery Centers," Interfaces, INFORMS, vol. 35(2), pages 133-139, April.
    2. 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.
    3. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    4. Chang, Mei-Shiang & Tseng, Ya-Ling & Chen, Jing-Wen, 2007. "A scenario planning approach for the flood emergency logistics preparation problem under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(6), pages 737-754, November.
    5. Yong Wang & Jiayi Zhe & Xiuwen Wang & Yaoyao Sun & Haizhong Wang, 2022. "Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows," Sustainability, MDPI, vol. 14(11), pages 1-37, May.
    6. Khalil Amine, 2019. "Multiobjective Simulated Annealing: Principles and Algorithm Variants," Advances in Operations Research, Hindawi, vol. 2019, pages 1-13, May.
    7. Muhammad Salman Habib & Young Hae Lee & Muhammad Saad Memon, 2016. "Mathematical Models in Humanitarian Supply Chain Management: A Systematic Literature Review," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-20, February.
    8. Brimberg, Jack & Juel, Henrik, 1998. "A bicriteria model for locating a semi-desirable facility in the plane," European Journal of Operational Research, Elsevier, vol. 106(1), pages 144-151, April.
    9. Hector R. Lim & Ma. Bernadeth B. Lim & Ann Wendy M. Rojas, 2022. "Towards modelling of evacuation behavior and planning for emergency logistics due to the Philippine Taal Volcanic eruption in 2020," 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. 114(1), pages 553-581, October.
    10. Mario Villalobos-Arias & Carlos Coello & Onésimo Hernández-Lerma, 2006. "Asymptotic convergence of a simulated annealing algorithm for multiobjective optimization problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 64(2), pages 353-362, October.
    11. Rosemary T. Berger & Collette R. Coullard & Mark S. Daskin, 2007. "Location-Routing Problems with Distance Constraints," Transportation Science, INFORMS, vol. 41(1), pages 29-43, February.
    12. 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.
    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. 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.
    2. 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.
    3. 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).
    4. Kundu, Tanmoy & Sheu, Jiuh-Biing & Kuo, Hsin-Tsz, 2022. "Emergency logistics management—Review and propositions for future research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    5. Sahar Validi & Arijit Bhattacharya & P. J. Byrne, 2020. "Sustainable distribution system design: a two-phase DoE-guided meta-heuristic solution approach for a three-echelon bi-objective AHP-integrated location-routing model," Annals of Operations Research, Springer, vol. 290(1), pages 191-222, July.
    6. Kılcı, Fırat & Kara, Bahar Yetiş & Bozkaya, Burçin, 2015. "Locating temporary shelter areas after an earthquake: A case for Turkey," European Journal of Operational Research, Elsevier, vol. 243(1), pages 323-332.
    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. 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.
    9. Li Zhu & Yeming Gong & Yishui Xu & Jun Gu, 2019. "Emergency Relief Routing Models for Injured Victims Considering Equity and Priority," Post-Print hal-02879681, HAL.
    10. Ali Torabi, S. & Shokr, Iman & Tofighi, Saeideh & Heydari, Jafar, 2018. "Integrated relief pre-positioning and procurement planning in humanitarian supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 113(C), pages 123-146.
    11. 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.
    12. Jafarzadeh-Ghoushchi, Saeid & Asghari, Mohammad & Mardani, Abbas & Simic, Vladimir & Tirkolaee, Erfan Babaee, 2023. "Designing an efficient humanitarian supply chain network during an emergency: A scenario-based multi-objective model," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    13. Maharjan, Rajali & Hanaoka, Shinya, 2020. "A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    14. 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).
    15. Li Zhu & Yeming Gong & Yishui Xu & Jun Gu, 2019. "Emergency relief routing models for injured victims considering equity and priority," Annals of Operations Research, Springer, vol. 283(1), pages 1573-1606, December.
    16. 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.
    17. 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).
    18. Ming Zhang & Yu Zhang & Zhifeng Qiu & Hanlin Wu, 2019. "Two-Stage Covering Location Model for Air–Ground Medical Rescue System," Sustainability, MDPI, vol. 11(12), pages 1-21, June.
    19. Maass, Kayse Lee & Trapp, Andrew C. & Konrad, Renata, 2020. "Optimizing placement of residential shelters for human trafficking survivors," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    20. 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.

    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:jsusta:v:15:y:2023:i:6:p:4882-:d:1092398. 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.