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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
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    References listed on IDEAS

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    1. Townsend, Robert M, 1994. "Risk and Insurance in Village India," Econometrica, Econometric Society, vol. 62(3), pages 539-591, May.
    2. 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.
    3. 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.
    4. repec:eme:mrn000:01409170910998255 is not listed on IDEAS
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
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