Volume 56, Number 2, March-April 2022
|751 - 768
|14 April 2022
An optimization approach for disaster relief network design under uncertainty and disruption with sustainability considerations
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
2 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
* Corresponding author: email@example.com
Accepted: 5 February 2022
Human-made, natural, and unexpected disasters always cause human and financial losses to communities. Disaster management is a framework with proven performance to reduce the damage caused by disaster and supply chain disruptions. Transferring the injured people from affected areas to hospitals at the minimum possible time is a crucial goal in times of disaster. This paper develops a two-stage stochastic programming model to transport the injured people from affected areas to hospitals in the incidence of multiple disruptions at transportation links and facilities under uncertainties. Herein, economic, social, and environmental aspects of sustainability are considered, while simultaneous disruptions are managed to minimize the adverse impacts of the disasters. We aim to determine optimal locations to establish transfer points and flows between the relief network nodes with sustainability considerations. Ultimately, a case study in District 12 of Tehran, Iran is conducted to ensure the proposed model’s validity and performance. Various sensitivity analyses are also implemented to ensure the model’s effectiveness. The results indicate that disruptions in facilities and transportation links lead to increased relief time, hence has the most significant negative impact on relief operations.
Mathematics Subject Classification: 90B06
Key words: Disruption / Disaster / Transfer Point / Stochastic Programming / Sustainability
© The authors. Published by EDP Sciences, ROADEF, SMAI 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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