JACIII Vol.21 No.7 pp. 1202-1210
doi: 10.20965/jaciii.2017.p1202


Optimizing the Arrangement of Post-Disaster Rescue Activities: An Agent-Based Simulation Approach

Shuang Chang*, Manabu Ichikawa**, Hiroshi Deguchi*, and Yasuhiro Kanatani**

*Tokyo Institute of Technology
4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8502, Japan

**National Institute of Public Health
2-3-6 Minami, Wako-shi, Saitama 351-0197, Japan

January 18, 2017
August 10, 2017
November 20, 2017
post-disaster management, resource allocation, agent-based simulation

This work aims to tackle the following two research questions regarding post-disaster rescues: how to optimize the rescue team dispatch based on the specialties of the team and the type of damage incurred, and how to optimize the allocation of injured patients to hospitals based on their symptoms, the rescue teams allocated, and the abilities of the hospitals to minimize fatalities. Rather than handling these two problems separately, we formulate them into an integrated system. A real-coded genetic algorithm is applied to minimize the estimated transport time in terms of distance, and the disparity between resource supply and demand. A set of scenarios is simulated and analyzed to provide insight for policy makers. Further, the simulated results can be used for future post-disaster medical assistance training.

Cite this article as:
S. Chang, M. Ichikawa, H. Deguchi, and Y. Kanatani, “Optimizing the Arrangement of Post-Disaster Rescue Activities: An Agent-Based Simulation Approach,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.7, pp. 1202-1210, 2017.
Data files:
  1. [1] [accessed Jan. 1, 2017]
  2. [2] Y. Koido, H. Kondo, M. Ichihara, Y. Kohayagawa, and H. Henmi, “Research on the DMAT response to the 2011 East Japan Earthquake,” Japan Institute of Public Health, Vol.60, No.6, pp. 495-501, 2011.
  3. [3] S. Chang, M. Ichikawa, H. Deguchi, and Y. Kanatani, “A General Framework of Resource Allocation Optimization and Dynamic Scheduling,” SICE J. of Control, Measurement, and System Integration, Vol.10, No.2, pp. 77-84, 2017.
  4. [4] H. Arora, T. Raghu, and A. Vinze, “Resourceallocationfordemand surge mitigation during disaster response,” Decision Support Systems, Vol.50, No.1, pp. 304-315, 2010.
  5. [5] K. Decker and J. Li, “Coordinated hospital patient scheduling,” Proc. Int. Conf. on Multi Agent Systems 1998, pp. 104-111, 1998.
  6. [6] F. Fiedrich, F. Gehbauer, and U. Rickers, “Optimized resource allocation for emergency response after earthquake disasters,” Safety Science, Vol.35, No.1-3, pp. 41-57, 2000.
  7. [7] M. C. Hoyos, R. S. Morales, and R. Akhavan-Tabatabaei, “OR models with stochastic components in disaster operations management: A literature survey,” Computers and Industrial Engineering, Vol.82, pp. 183-197, 2015.
  8. [8] M. Muaafa and J. E. Ramirez-Marquez, “Emergency resources allocation for disaster response: An evolutionary approach,” IN-FORMS Annual Meeting 2013, 2013.
  9. [9] Y. J. Zheng, S. Y. Chen, and H. F. Lin, “Evolutionary optimization for disaster relief operations: A survey,” Applied Soft Computing, Vol.27, pp. 553-566, 2015.
  10. [10] J. H. Holland, “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology,” Control and Artificial Intelligence, MIT Press, 1975.
  11. [11] J. Kennedy and R. Eberhart, “Particle swarm optimization,” IEEE Int. Conf. on Neural Networks, Vol.4, pp. 1942-1948, 1995.
  12. [12] M. R. Bonyadi and Z. Michalewicz, “Particle swarm optimization for single objective continuous space problems: a review,” Evolutionary computation, Vol.25, No.4, pp. 1-54, 2017.
  13. [13] C. R. Houck, J. A. Joines, and M. G. Kay, “A Genetic Algorithm for Function Optimization: A Matlab Implementation,” NSCU-IE TR 95-09, North Carolina State Univ., 1995.
  14. [14] I. Ono and S. Kobayashi, “A real coded genetic algorithm for function optimization using uni- modal normal distributed crossover,” Proc. of the 7th Int. Conf. on Genetic Algorithms, pp. 246-253, 1997.
  15. [15] H. Sato, I. Ono and S. Kobayashi, “A New Generation Alternation Model of Genetic Algorithms and Its Assessment,” J. of the Japanese Society for Artificial Intelligence, Vol.12, No.5, pp. 734-735, 1997.
  16. [16] [accessed Jan. 1, 2017]
  17. [17] T. Murata, H. Ishibuchi, and H. Tanaka, “Multi-objective genetic algorithm and its applications to flowshop scheduling,” Computers ind. Engng, Vol.30, No.4, pp. 957-968, 1996.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Aug. 14, 2018