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A fuzzy sustainable model for COVID-19 medical waste supply chain network

Author

Listed:
  • Fariba Goodarzian

    (Heriot-Watt University)

  • Peiman Ghasemi

    (University of Vienna)

  • Angappa Gunasekaran

    (California State University)

  • Ashraf Labib

    (University of Portsmouth)

Abstract

The COVID-19 has placed pandemic modeling at the forefront of the whole world’s public policymaking. Nonetheless, forecasting and modeling the COVID-19 medical waste with a detoxification center of the COVID-19 medical wastes remains a challenge. This work presents a Fuzzy Inference System to forecast the COVID-19 medical wastes. Then, people are divided into five categories are divided according to the symptoms of the disease into healthy people, suspicious, suspected of mild COVID-19, and suspicious of intense COVID-19. In this regard, a new fuzzy sustainable model for COVID-19 medical waste supply chain network for location and allocation decisions considering waste management is developed for the first time. The main purpose of this paper is to minimize supply chain costs, the environmental impact of medical waste, and to establish detoxification centers and control the social responsibility centers in the COVID-19 outbreak. To show the performance of the suggested model, sensitivity analysis is performed on important parameters. A real case study in Iran/Tehran is suggested to validate the proposed model. Classifying people into different groups, considering sustainability in COVID 19 medical waste supply chain network and examining new artificial intelligence methods based on TS and GOA algorithms are among the contributions of this paper. Results show that the decision-makers should use an FIS to forecast COVID-19 medical waste and employ a detoxification center of the COVID-19 medical wastes to reduce outbreaks of this pandemic.

Suggested Citation

  • Fariba Goodarzian & Peiman Ghasemi & Angappa Gunasekaran & Ashraf Labib, 2024. "A fuzzy sustainable model for COVID-19 medical waste supply chain network," Fuzzy Optimization and Decision Making, Springer, vol. 23(1), pages 93-127, March.
    • Handle: RePEc:spr:fuzodm:v:23:y:2024:i:1:d:10.1007_s10700-023-09412-8
    DOI: 10.1007/s10700-023-09412-8
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