TY - GEN
T1 - An agent-based simulation of post-disaster relief and medical assistance activities
AU - Chang, Shuang
AU - Ichikawa, Manabu
AU - Deguchi, Hiroshi
AU - Kanatani, Yasuhiro
N1 - Publisher Copyright:
© 2016, Fuji Technology Press. All rights reserved.
PY - 2016
Y1 - 2016
N2 - Disaster management is commonly defined by four phases as planning, mitigation, response and recovery, of which the response phase is a critical yet challenging one to minimize fatalities under tight time and scarce resource constraints. Especially for the post-disaster medical assistance activities, how to optimize the rescue team dispatch based on their specialties and the damaged condition, and how to optimize the allocation of injured patients to hospitals based on their symptoms and hospital abilities, considering hospital capacities and transportation distance to minimize the fatalities, are forever crucial issues. Rather than handling above-mentioned problems separately, we aim to tackle them as part of an agent-based framework by which the resource allocation and subsequent inlocation resource scheduling are treated as a holistic system. This work only focuses on the two dispatching and allocation phases of which we apply real-coded genetic algorithm to minimize the total time cost of transportation in terms of distance and the disparity between the demand and supply of resources. The scenario analysis based on the simulated results could be used for post-disaster medical assistant training purposes and provide insights for policy-makers in future work.
AB - Disaster management is commonly defined by four phases as planning, mitigation, response and recovery, of which the response phase is a critical yet challenging one to minimize fatalities under tight time and scarce resource constraints. Especially for the post-disaster medical assistance activities, how to optimize the rescue team dispatch based on their specialties and the damaged condition, and how to optimize the allocation of injured patients to hospitals based on their symptoms and hospital abilities, considering hospital capacities and transportation distance to minimize the fatalities, are forever crucial issues. Rather than handling above-mentioned problems separately, we aim to tackle them as part of an agent-based framework by which the resource allocation and subsequent inlocation resource scheduling are treated as a holistic system. This work only focuses on the two dispatching and allocation phases of which we apply real-coded genetic algorithm to minimize the total time cost of transportation in terms of distance and the disparity between the demand and supply of resources. The scenario analysis based on the simulated results could be used for post-disaster medical assistant training purposes and provide insights for policy-makers in future work.
KW - Agent-based approach
KW - Post-disaster management
KW - Resource allocation
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M3 - Conference contribution
AN - SCOPUS:84997712039
T3 - ISCIIA 2016 - 7th International Symposium on Computational Intelligence and Industrial Applications
BT - ISCIIA 2016 - 7th International Symposium on Computational Intelligence and Industrial Applications
PB - Fuji Technology Press
T2 - 7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016
Y2 - 3 November 2016 through 6 November 2016
ER -