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A sustainable-resilience healthcare network for handling COVID-19 pandemic

Author

Listed:
  • Fariba Goodarzian

    (University of Tehran
    Scientific Network for Innovation and Research Excellence)

  • Peiman Ghasemi

    (Islamic Azad University)

  • Angappa Gunasekaren

    (Penn State Harrisburg)

  • Ata Allah Taleizadeh

    (University of Tehran)

  • Ajith Abraham

    (Innopolis University
    Scientific Network for Innovation and Research Excellence)

Abstract

In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and multi-echelon mixed-integer linear programming model for the current network is formulated and designed. Formulating a new MILP model to design a sustainable-resilience healthcare network during the COVID-19 pandemic and developing three hybrid meta-heuristic algorithms are among the most important contributions of this research. In order to estimate the values of the required demand for medicines, the simulation approach is employed. To cope with uncertain parameters, stochastic chance-constraint programming is proposed. This paper also proposed three meta-heuristic methods including Multi-Objective Teaching–learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. Since heuristic approaches are sensitive to input parameters, the Taguchi approach is suggested to control and tune the parameters. A comparison is performed by using eight assessment metrics to validate the quality of the obtained Pareto frontier by the heuristic methods on the experiment problems. To validate the current model, a set of sensitivity analysis on important parameters and a real case study in the United States are provided. Based on the empirical experimental results, computational time and eight assessment metrics proposed methodology seems to work well for the considered problems. The results show that by raising the transportation costs, the total cost and the environmental impacts of sustainability increased steadily and the trend of the social responsibility of staff rose gradually between − 20 and 0%, but, dropped suddenly from 0 to + 20%. Also in terms of the on-resiliency of the proposed network, the trends climbed slightly and steadily. Applications of this paper can be useful for hospitals, pharmacies, distributors, medicine manufacturers and the Ministry of Health.

Suggested Citation

  • Fariba Goodarzian & Peiman Ghasemi & Angappa Gunasekaren & Ata Allah Taleizadeh & Ajith Abraham, 2022. "A sustainable-resilience healthcare network for handling COVID-19 pandemic," Annals of Operations Research, Springer, vol. 312(2), pages 761-825, May.
    • Handle: RePEc:spr:annopr:v:312:y:2022:i:2:d:10.1007_s10479-021-04238-2
    DOI: 10.1007/s10479-021-04238-2
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    References listed on IDEAS

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    1. Nezamoddini, Nasim & Gholami, Amirhosein & Aqlan, Faisal, 2020. "A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks," International Journal of Production Economics, Elsevier, vol. 225(C).
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    Cited by:

    1. Lu, Changxiang & Ye, Yong & Fang, Yongjun & Fang, Jiaqi, 2023. "An optimal control theory approach for freight structure path evolution post-COVID-19 pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    2. Emre Berk & Onurcan Ayas & M. Ali Ülkü, 2023. "Optimizing Process-Improvement Efforts for Supply Chain Operations under Disruptions: New Structural Results," Sustainability, MDPI, vol. 15(17), pages 1-23, August.
    3. Yılmaz, Ömer Faruk & Yeni, Fatma Betül & Gürsoy Yılmaz, Beren & Özçelik, Gökhan, 2023. "An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).

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