Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1202-1210 |
| Number of pages | 9 |
| Journal | Journal of Advanced Computational Intelligence and Intelligent Informatics |
| Volume | 21 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2017 Nov 1 |
| Externally published | Yes |
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Keywords
- Agent-based simulation
- Post-disaster management
- Resource allocation
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Artificial Intelligence
Cite this
Optimizing the arrangement of post-disaster rescue activities : An agent-based simulation approach. / Chang, Shuang; Ichikawa, Manabu; Deguchi, Hiroshi; Kanatani, Yasuhiro.
In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 21, No. 7, 01.11.2017, p. 1202-1210.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Optimizing the arrangement of post-disaster rescue activities
T2 - An agent-based simulation approach
AU - Chang, Shuang
AU - Ichikawa, Manabu
AU - Deguchi, Hiroshi
AU - Kanatani, Yasuhiro
PY - 2017/11/1
Y1 - 2017/11/1
N2 - 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.
AB - 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.
KW - Agent-based simulation
KW - Post-disaster management
KW - Resource allocation
UR - http://www.scopus.com/inward/record.url?scp=85037149357&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85037149357&partnerID=8YFLogxK
U2 - 10.20965/jaciii.2017.p1202
DO - 10.20965/jaciii.2017.p1202
M3 - Article
AN - SCOPUS:85037149357
VL - 21
SP - 1202
EP - 1210
JO - Journal of Advanced Computational Intelligence and Intelligent Informatics
JF - Journal of Advanced Computational Intelligence and Intelligent Informatics
SN - 1343-0130
IS - 7
ER -