A Multi-Period Robust Model for Fair Allocation of Rescue Resources After Natural Disasters

Document Type : Research Paper

Authors

1 School of Industrial Engineering, College of Engineering, University of Tehran

2 School of Industrial Engineering, Sharif University of Technology

Abstract

Effective planning and execution of rescue operations play a vital role in the survival rate of impacted people in natural disasters. Given the scarcity of resources vs. the rescue demand magnitude and the time pressure in the operations, this becomes even more crucial in saving more lives. This paper presents a mixed integer multi-period optimization model for rescue teams’ allocation in the disaster-affected districts. The presented model maximizes the minimum effective coverage of demand over the impacted districts as the fairness satisfaction. Due to the sensitivity of operations to the time loss, the allocation decision in each period is characterized by an effectiveness measure. To count for the coordination of teams in the response operations, transshipment of resources between affected districts and the possibility of releasing them from rescue operations to other missions are considered. To count for the uncertain disaster environment, an interval-data robust optimization is adopted. Being inspired by the results of earthquake floating scenario in Tehran region 6, the mathematical model is run and analyzed. The results analysis highlights the importance of training local teams and planning for neighbor regions’ resource back-up to provide timely availability of rescue teams. The developed model is also capable of providing the disaster managers in earlier stages of disaster management cycle with practical managerial insights for assessing and prioritizing their options for resource capacity building and structures’ resistance to the earthquake.

Keywords

References

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Iranian Journal of Trade Studies
Volume 25, Issue 97 - Serial Number 97
January 2021
Pages 233-257

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History

  • Receive Date: 08 September 2018
  • Revise Date: 04 November 2019
  • Accept Date: 11 November 2019

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