Presenting Mathematical Programming Model for a Sustainable Resilient Closed Loop Supply Chain Considering the Response Criteria and the Uncertain Effects of Covid-19

Document Type : Research/ Original/ Regular Article

Authors

1 Ph.D. Candidate in Industrial Engineering, Science and Research Branch, Islamic Azad University, (IAU), Tehran, Iran

2 Department of Management, Shahid Beheshti University, Tehran, Iran.

3 Department of Information Technology, Iranian Research Institute for information Science and Technology (IRANDOC), Tehran, Iran.

4 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran

Abstract

The aim of the current research is to develop a mathematical programming model to provide a resilience considering the quality of response and non-determinism of covid-19. For this purpose, library studies of a three-objective chain model have been designed with regard to the objectives of sustainability and resilience. The first goal includes resilience and cost, and the next two goals include environmental and social responsibility. Then the model was analyzed using NSGA-III meta-heuristic algorithm. The results show that applying resilience and sustainability leads to better use of materials, purchase of less materials, more recycling, and less disposal of materials, which benefits the supply chain. on the other hand, considering the uncertainty in the model, the results show that uncertainty has an effect on all three objective functions and leads to its deterioration. Regarding the realization of accountability, it is concluded that the realization of accountability requires more cost, intensifying environmental issues and of course improving social responsibility. That is, the more social responsibility increases, the more accountability, but increasing accountability requires increasing costs and worsening environmental issues. Finally, the effective strategies for improving the supply chain during the Covid era were examined, and the capacity increase strategies were determined as the best strategies.
 
 

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References

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History

  • Receive Date: 06 November 2022
  • Revise Date: 08 May 2023
  • Accept Date: 31 May 2023

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