A Hybrid Genetic Algorithm for Multi-emergency Medical Service Center Location-allocation Problem in Disaster Response

Authors

  • Xuehong Gao
  • Yanjie Zhou
  • Muhammad Idil Haq Amir
  • Fifi Alfiana Rosyidah
  • Gyu M. Lee

DOI:

https://doi.org/10.23055/ijietap.2017.24.6.4299

Abstract

Temporary emergency medical service center provides an expeditious and appropriate medical treatment for injured patients in the post-disaster. As part of the first responders in quick response to disaster relief, temporary emergency medical service center plays a significant role in enhancing survival, controlling mortality and preventing disability. In this study, the final patient mortality risk value (injury severity) caused by both initial mortality risk value and travel distance (travel time) is considered to determine the location-allocation of temporary emergency medical service centers. In order to improve effective rescue task in post-disaster, two objectives of models are developed. The objectives include minimize the total travel time and the total mortality risk value of patients in the whole disaster area. Then, genetic algorithm with modified fuzzy C-means clustering algorithm is developed to decide locations and allocations of temporary emergency medical service centers. Illustrative examples are given to show how the proposed models optimize the locations and allocations of temporary emergency medical service centers and handle post-earthquake emergencies in the Portland area. Furthermore, comparisons of the results are presented to show the advantages of the proposed algorithm in minimizing the total travel time and the total mortality risk value for temporary emergency medical service centers in disaster response.

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Published

2018-01-11

How to Cite

Gao, X., Zhou, Y., Amir, M. I. H., Rosyidah, F. A., & Lee, G. M. (2018). A Hybrid Genetic Algorithm for Multi-emergency Medical Service Center Location-allocation Problem in Disaster Response. International Journal of Industrial Engineering: Theory, Applications and Practice, 24(6). https://doi.org/10.23055/ijietap.2017.24.6.4299

Issue

Vol. 24 No. 6 (2017)

Section

Operations Research

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