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Optimal material distribution decisions based on epidemic diffusion rule and stochastic latent period for emergency rescue

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  • Haiyan Wang
  • Xinping Wang
  • Amy Z. Zeng

Abstract

Demand of emergency materials is usually uncertain and varies quickly as the latent period changes. With the consideration of the delay caused by the latent period of an epidemic, we construct a multi-objective stochastic programming model with time-varying demand for the emergency logistics network based on the epidemic diffusion rule. The genetic algorithm coupled with Monte Carlo simulation is adopted to solve the optimisation model, and the application of the model as well as a sensitivity analysis of the latent period is given by a numerical example.

Suggested Citation

  • Haiyan Wang & Xinping Wang & Amy Z. Zeng, 2009. "Optimal material distribution decisions based on epidemic diffusion rule and stochastic latent period for emergency rescue," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 1(1/2), pages 76-96.
    • Handle: RePEc:ids:ijmore:v:1:y:2009:i:1/2:p:76-96
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    Citations

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    Cited by:

    1. Ubaid Illahi & Mohammad Shafi Mir, 2021. "Maintaining efficient logistics and supply chain management operations during and after coronavirus (COVID-19) pandemic: learning from the past experiences," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 11157-11178, August.
    2. 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.
    3. Xiaoyan Xu & Suresh P. Sethi & Sai‐Ho Chung & Tsan‐Ming Choi, 2023. "Reforming global supply chain management under pandemics: The GREAT‐3Rs framework," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 524-546, February.
    4. Biswas, Debajyoti & Alfandari, Laurent, 2022. "Designing an optimal sequence of non‐pharmaceutical interventions for controlling COVID-19," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1372-1391.
    5. Chunxia Hou & Huiyuan Jiang, 2021. "Methodology of emergency medical logistics for multiple epidemic areas in public health emergency," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-23, July.
    6. Linus Nyiwul, 2021. "Epidemic Control and Resource Allocation: Approaches and Implications for the Management of COVID-19," Studies in Microeconomics, , vol. 9(2), pages 283-305, December.
    7. Pan, Yuqing & Cheng, T.C.E. & He, Yuxuan & Ng, Chi To & Sethi, Suresh P., 2022. "Foresighted medical resources allocation during an epidemic outbreak," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    8. Muhammad Umar Farooq & Amjad Hussain & Tariq Masood & Muhammad Salman Habib, 2021. "Supply Chain Operations Management in Pandemics: A State-of-the-Art Review Inspired by COVID-19," Sustainability, MDPI, vol. 13(5), pages 1-33, February.
    9. Hanane Allioui & Azzeddine Allioui & Youssef Mourdi, 2024. "Maintaining effective logistics management during and after COVID‑19 pandemic: survey on the importance of artificial intelligence to enhance recovery strategies," OPSEARCH, Springer;Operational Research Society of India, vol. 61(2), pages 918-962, June.
    10. Hao Yu & Xu Sun & Wei Deng Solvang & Xu Zhao, 2020. "Reverse Logistics Network Design for Effective Management of Medical Waste in Epidemic Outbreaks: Insights from the Coronavirus Disease 2019 (COVID-19) Outbreak in Wuhan (China)," IJERPH, MDPI, vol. 17(5), pages 1-25, March.

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