IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v8y2024i1p9-d1317301.html
   My bibliography  Save this article

Efficient Humanitarian Logistics: Multi-Commodity Location–Inventory Model Incorporating Demand Probability and Consumption Coefficients

Author

Listed:
  • Majid Mehrabi Delshad

    (Department of Industrial Engineer, Engineering Faculty, Parand Branch, Islamic Azad University, Parand 3761396361, Iran)

  • Adel Pourghader Chobar

    (Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin 341851416, Iran)

  • Peiman Ghasemi

    (Department of Business Decisions and Analytics, University of Vienna, Kolingasse 14-16, 1090 Vienna, Austria)

  • Davoud Jafari

    (Department of Industrial Engineer, Engineering Faculty, Parand Branch, Islamic Azad University, Parand 3761396361, Iran)

Abstract

Background: A logistics network plan could be a major key issue due to its effect on supply chain effectiveness and responsiveness. This study aims to investigate the inventory location in the humanitarian logistics response stage using a three-level logistics network to integrate location–allocation problems such as warehouse location and shelter allocation to each facility, and then determine the inventory level in each warehouse. Methods : In this research, the center and its distribution, as well as the reduction in service-level costs due to inventory deficit, have been considered to increase the level of shelter services. In order to investigate the network, in this study, bi-objective mixed-integer linear programming (BOMILP) is presented. Results : The first objective is to reduce location costs and inventory costs that take into account probable demand, consumption factors, and transportation costs, and the second objective is to raise the level of services offered to victims in the model. The software programs GAMS win32, 25.1.2 and MATLAB have been utilized with numerical examples in various dimensions. Conclusions : To maximize the efficiency and quality of the service, first, the model was numerically solved, and then the location where the most commodities could be transported at the lowest possible cost was identified.

Suggested Citation

  • Majid Mehrabi Delshad & Adel Pourghader Chobar & Peiman Ghasemi & Davoud Jafari, 2024. "Efficient Humanitarian Logistics: Multi-Commodity Location–Inventory Model Incorporating Demand Probability and Consumption Coefficients," Logistics, MDPI, vol. 8(1), pages 1-20, January.
  • Handle: RePEc:gam:jlogis:v:8:y:2024:i:1:p:9-:d:1317301
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/8/1/9/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2305-6290/8/1/9/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kawase, Riki & Iryo, Takamasa, 2023. "Optimal stochastic inventory-distribution strategy for damaged multi-echelon humanitarian logistics network," European Journal of Operational Research, Elsevier, vol. 309(2), pages 616-633.
    2. Townsend, Robert M, 1994. "Risk and Insurance in Village India," Econometrica, Econometric Society, vol. 62(3), pages 539-591, May.
    3. Melo, M.T. & Nickel, S. & Saldanha-da-Gama, F., 2009. "Facility location and supply chain management - A review," European Journal of Operational Research, Elsevier, vol. 196(2), pages 401-412, July.
    4. L N Van Wassenhove, 2006. "Humanitarian aid logistics: supply chain management in high gear," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(5), pages 475-489, May.
    5. Pérez-Galarce, Francisco & Canales, Linda J. & Vergara, Claudio & Candia-Véjar, Alfredo, 2017. "An optimization model for the location of disaster refuges," Socio-Economic Planning Sciences, Elsevier, vol. 59(C), pages 56-66.
    6. Cotes, Nathalie & Cantillo, Victor, 2019. "Including deprivation costs in facility location models for humanitarian relief logistics," Socio-Economic Planning Sciences, Elsevier, vol. 65(C), pages 89-100.
    7. Tancrez, Jean-Sébastien & Lange, Jean-Charles & Semal, Pierre, 2012. "A location-inventory model for large three-level supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(2), pages 485-502.
    8. Loree, Nick & Aros-Vera, Felipe, 2018. "Points of distribution location and inventory management model for Post-Disaster Humanitarian Logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 1-24.
    9. 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.
    10. repec:eme:mrn000:01409170910998255 is not listed on IDEAS
    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. 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).
    3. 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.
    4. Laijun Zhao & Huiyong Li & Yan Sun & Rongbing Huang & Qingmi Hu & Jiajia Wang & Fei Gao, 2017. "Planning Emergency Shelters for Urban Disaster Resilience: An Integrated Location-Allocation Modeling Approach," Sustainability, MDPI, vol. 9(11), pages 1-20, November.
    5. Schuster Puga, Matías & Tancrez, Jean-Sébastien, 2017. "A heuristic algorithm for solving large location–inventory problems with demand uncertainty," European Journal of Operational Research, Elsevier, vol. 259(2), pages 413-423.
    6. Govindan, Kannan & Mina, Hassan & Alavi, Behrouz, 2020. "A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    7. Khanchehzarrin, Saeed & Ghaebi Panah, Mona & Mahdavi-Amiri, Nezam & Shiripour, Saber, 2022. "A bi-level multi-objective location-routing optimization model for disaster relief operations considering public donations," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    8. Thomas Hacardiaux & Christof Defryn & Jean-Sébastien Tancrez & Lotte Verdonck, 2022. "Balancing partner preferences for logistics costs and carbon footprint in a horizontal cooperation," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 121-153, March.
    9. Shuangyan Li & Xialian Li & Dezhi Zhang & Lingyun Zhou, 2017. "Joint Optimization of Distribution Network Design and Two-Echelon Inventory Control with Stochastic Demand and CO2 Emission Tax Charges," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-22, January.
    10. 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).
    11. Yijun Shi & Guofang Zhai & Lihua Xu & Quan Zhu & Jinyang Deng, 2019. "Planning Emergency Shelters for Urban Disasters: A Multi-Level Location–Allocation Modeling Approach," Sustainability, MDPI, vol. 11(16), pages 1-19, August.
    12. Pourya Pourhejazy & Oh Kyoung Kwon, 2016. "The New Generation of Operations Research Methods in Supply Chain Optimization: A Review," Sustainability, MDPI, vol. 8(10), pages 1-23, October.
    13. Mittal, Neha & Boile, Maria & Baveja, Alok & Theofanis, Sotiris, 2013. "Determining optimal inland-empty-container depot locations under stochastic demand," Research in Transportation Economics, Elsevier, vol. 42(1), pages 50-60.
    14. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    15. Sun, Huali & Li, Jiamei & Wang, Tingsong & Xue, Yaofeng, 2022. "A novel scenario-based robust bi-objective optimization model for humanitarian logistics network under risk of disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    16. Lijo John & Anand Gurumurthy & Arqum Mateen & Gopalakrishnan Narayanamurthy, 2022. "Improving the coordination in the humanitarian supply chain: exploring the role of options contract," Annals of Operations Research, Springer, vol. 319(1), pages 15-40, December.
    17. Baharmand, Hossein & Comes, Tina & Lauras, Matthieu, 2019. "Bi-objective multi-layer location–allocation model for the immediate aftermath of sudden-onset disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 86-110.
    18. Pérez-Galarce, Francisco & Canales, Linda J. & Vergara, Claudio & Candia-Véjar, Alfredo, 2017. "An optimization model for the location of disaster refuges," Socio-Economic Planning Sciences, Elsevier, vol. 59(C), pages 56-66.
    19. 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.
    20. Seongtae Kim & M. Ramkumar & Nachiappan Subramanian, 2019. "Logistics service provider selection for disaster preparation: a socio-technical systems perspective," Annals of Operations Research, Springer, vol. 283(1), pages 1259-1282, 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:jlogis:v:8:y:2024:i:1:p:9-:d:1317301. 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.