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A data-driven optimization model to response to COVID-19 pandemic: a case study

Author

Listed:
  • Amin Eshkiti

    (College of Engineering, University of Tehran)

  • Fatemeh Sabouhi

    (College of Engineering, University of Tehran)

  • Ali Bozorgi-Amiri

    (College of Engineering, University of Tehran)

Abstract

COVID-19 is a highly prevalent disease that has led to numerous predicaments for healthcare systems worldwide. Owing to the significant influx of patients and limited resources of health services, there have been several limitations associated with patients' hospitalization. These limitations can cause an increment in the COVID-19-related mortality due to the lack of appropriate medical services. They can also elevate the risk of infection in the rest of the population. The present study aims to investigate a two-phase approach to designing a supply chain network for hospitalizing patients in the existing and temporary hospitals, efficiently distributing medications and medical items needed by patients, and managing the waste created in hospitals. Since the number of future patients is uncertain, in the first phase, trained Artificial Neural Networks with historical data forecast the number of patients in future periods and generate scenarios. Through the use of the K-Means method, these scenarios are reduced. In the second phase, a multi-objective, multi-period, data-driven two-stage stochastic programming is developed using the acquired scenarios in the previous phase concerning the uncertainty and disruption in facilities. The objectives of the proposed model include maximizing the minimum allocation-to-demand ratio, minimizing the total risk of disease spread, and minimizing the total transportation time. Furthermore, a real case study is investigated in Tehran, the capital of Iran. The results showed that the areas with the highest population density and no facilities near them have been selected for the location of temporary facilities. Among temporary facilities, temporary hospitals can allocate up to 2.6% of the total demand, which puts pressure on the existing hospitals to be removed. Furthermore, the results indicated that the allocation-to-demand ratio can remain at an ideal level when disruptions occur by considering temporary facilities. Our analyses focus on: (1) Examining demand forecasting error and generated scenarios in the first phase, (2) exploring the impact of demand parameters on the allocation-to-demand ratio, total time and total risk, (3) investigating the strategy of utilizing temporary hospitals to address sudden changes in demand, (4) evaluating the effect of disruption to facilities on the supply chain network.

Suggested Citation

  • Amin Eshkiti & Fatemeh Sabouhi & Ali Bozorgi-Amiri, 2023. "A data-driven optimization model to response to COVID-19 pandemic: a case study," Annals of Operations Research, Springer, vol. 328(1), pages 337-386, September.
    • Handle: RePEc:spr:annopr:v:328:y:2023:i:1:d:10.1007_s10479-023-05320-7
    DOI: 10.1007/s10479-023-05320-7
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    1. Zhao, Jun & Huang, Lixia & Lee, Der-Horng & Peng, Qiyuan, 2016. "Improved approaches to the network design problem in regional hazardous waste management systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 52-75.
    2. Cohen, Jennifer & Rodgers, Yana, 2020. "Contributing Factors to Personal Protective Equipment Shortages during the COVID-19 Pandemic," MPRA Paper 103496, University Library of Munich, Germany.
    3. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    4. Snoeck, André & Udenio, Maximiliano & Fransoo, Jan C., 2019. "A stochastic program to evaluate disruption mitigation investments in the supply chain," European Journal of Operational Research, Elsevier, vol. 274(2), pages 516-530.
    5. Mohammad Asghari & Amir M. Fathollahi-Fard & S. M. J. Mirzapour Al-e-hashem & Maxim A. Dulebenets, 2022. "Transformation and Linearization Techniques in Optimization: A State-of-the-Art Survey," Mathematics, MDPI, vol. 10(2), pages 1-26, January.
    6. 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.
    7. Raj, Alok & Mukherjee, Abheek Anjan & de Sousa Jabbour, Ana Beatriz Lopes & Srivastava, Samir K., 2022. "Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned," Journal of Business Research, Elsevier, vol. 142(C), pages 1125-1139.
    8. Govindan, Kannan & Fattahi, Mohammad, 2017. "Investigating risk and robustness measures for supply chain network design under demand uncertainty: A case study of glass supply chain," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 680-699.
    9. Alberto Godio & Francesca Pace & Andrea Vergnano, 2020. "SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence," IJERPH, MDPI, vol. 17(10), pages 1-19, May.
    10. Gholami-Zanjani, Seyed Mohammad & Klibi, Walid & Jabalameli, Mohammad Saeed & Pishvaee, Mir Saman, 2021. "The design of resilient food supply chain networks prone to epidemic disruptions," International Journal of Production Economics, Elsevier, vol. 233(C).
    11. Gilani, Hani & Sahebi, Hadi, 2022. "A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain," Omega, Elsevier, vol. 110(C).
    12. Sanjay Mehrotra & Hamed Rahimian & Masoud Barah & Fengqiao Luo & Karolina Schantz, 2020. "A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID‐19," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(5), pages 303-320, August.
    13. Mavrotas, George & Florios, Kostas, 2013. "An improved version of the augmented epsilon-constraint method (AUGMECON2) for finding the exact Pareto set in Multi-Objective Integer Programming problems," MPRA Paper 105034, University Library of Munich, Germany.
    14. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
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