A hybrid robust optimization and simulation model to establish temporary emergency stations for earthquake relief

Articles in Press

Document Type : Research Note

Authors

1 Industrial Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 School of Strategy and Leadership, Faculty of Business and Law, Coventry University, Coventry, UK CV1 5DL, Coventry University

3 Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, Iran

Abstract

Natural disasters such as earthquakes pose a constant threat to human communities. A key measure in planning and preparedness against disasters is to determine the optimal location of temporary emergency stations (TESs) and allocate them to the affected areas. Decisions in the preparedness phase ensure the optimal performance of TESs and minimize potential delays in the course of rescue operations. During crises, TESs may have a significant role in minimizing human causalities. In this research, a robust simulation-optimization approach is proposed to ensure appropriate planning in the preparedness phase. We develop a mathematical model for simultaneous and hierarchical location-allocation of the injured to the available medical facilities under disaster conditions. Given the inherent unpredictability of natural disasters, the uncertainty of the data should inevitably be taken into account. We thus employ a robust optimization technique to tackle the uncertainty in the number of the injured and utilize simulation to create the first seven days of the crisis and determine the optimal capacity of the medical facilities. The findings indicate that by eliminating the unnecessary transfer of mildly-injured victims to high-level medical facilities, the model brings about a 15 percent reduction in treatment costs.

Keywords

References

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Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 01 August 2022

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History

  • Receive Date: 26 November 2020
  • Revise Date: 20 March 2022
  • Accept Date: 01 August 2022

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