Multi-Objective Optimization of Sustainable Reverse Supply Chain for Municipal Solid Waste Considering Uncertainty: Robust Optimization Approach

Document Type : Research/ Original/ Regular Article

Authors

1 Master's Degree, Department of Management, Faculty of Management and Finance, Khatam University, Tehran, Iran.

2 Assistant Professor, Department of Management, Faculty of Management and Finance, Khatam University, Tehran, Iran.

Abstract

In recent years, waste management has become a serious challenge due to the increase in population, urbanization, and waste production. Improper waste management not only damages the environment and increases pollution, but also increases economic costs and has negative effects on human health and quality of life. Therefore, designing a sustainable supply chain that simultaneously considers economic, environmental, and social dimensions is very important. Also, due to the existence of uncertainties such as changes in the volume of waste produced and supply chain costs, it is essential to consider these factors in waste management. The aim of this research is to develop a mathematical model of a sustainable reverse supply chain network for municipal solid waste management. In this model, the parameters of transportation costs between centers are considered as uncertain parameters. Also, the robust optimization approach and the Bertsims-Wasim method have been used to consider uncertainty. Finally, to verify the validity of the model, a numerical example has been solved, which, due to the multi-objective nature of the model, has been solved using the epsilon constraint method. Based on the results, the designed model can reduce waste management costs, including waste transportation costs and operational costs such as collection, separation, processing, and recycling.

Keywords

Main Subjects

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References

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Supply Chain Management
Volume 27, Issue 88 - Serial Number 88
Serial number 88. Autumn 2025
November 2025
Pages 1-34

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History

  • Receive Date: 02 February 2025
  • Revise Date: 07 April 2025
  • Accept Date: 06 September 2025
  • Publish Date: 21 November 2025

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