Locating Temporary Relief Centers and Dynamic Routing Air Rescue Vehicles in Times of Crisis

Document Type : Research Paper

Authors

1 MSC. Grad., Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran

2 Assistant Professor, Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran

3 Assistant professor, Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran

Abstract

This paper presents a new mathematical model for location of temporary relief centers and dynamic routing of aerial rescue vehicles distributing basic supplies in relief operations. The objective function of the proposed model minimizes the time required to distribute the supplies among the designated relief centers. The proposed model seeks to locate the relief centers in a way that all affected areas get covered by at least one relief center. Considering the importance of quick action amid a post-disaster environment with characteristics such as uncertain demand for relief supplies, inaccurate information regarding victims, aftershocks, and extensively damaged road networks, in the proposed model, location and level of demand are considered to be dynamic, and relief supplies are assumed to be distributed by aerial transport vehicles. The assessed problem is of NP-Hard complexity, so, this paper also presents a scatter search and genetic algorithm to obtain its solutions. To evaluate the performance of the proposed algorithm, it is tested and compared with an exact method and other meta-heuristic algorithms. In small test problems, the average solving time for exact method, genetic algorithm and scatter search algorithm were 669.8, 54.7, and 56.2 respectively.  In view of solutions quality, the average percentage error for genetic algorithm and scatter search algorithm were 3.8 and 4.1 respectively. In the large scale problems, genetic algorithm reached to the better solution in 17 problems from 27 test problems compared to the scatter search algorithm. The results indicate the good performance of proposed algorithms.

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References

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History

  • Receive Date: 26 August 2016
  • Revise Date: 13 May 2017
  • Accept Date: 15 May 2017

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