TY - JOUR
T1 - Vulnerability analysis of evacuation transportation networks
AU - Kim, Jun
AU - Lee, Je Hun
AU - Kim, Hyun Jung
AU - Do Chung, Byung
N1 - Publisher Copyright:
© 2018 International Journal of Industrial Engineering.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - As natural disasters are increasing in frequency and scale, there is increasing interest in determining appropriate reactions to the disasters, such as evacuation planning and disaster relief distribution. When an evacuation network is built, strategic analyses are needed to support its operations. One of these is a vulnerability analysis to determine the weak points of an evacuation transportation network is one of those. This study aims to analyze the vulnerability of evacuation transportation networks constructed using the cell transmission model. Previous studies have considered all the possible scenarios to investigate weak points without using mathematical approaches. In this paper, we propose a bi-level optimization model to identify the fragile spots in a network. Due to the complexity of the problem, a genetic algorithm combined with linear programming is developed to find good solutions in a reasonable computational time. Solutions from the proposed algorithm are compared with optimal solutions.
AB - As natural disasters are increasing in frequency and scale, there is increasing interest in determining appropriate reactions to the disasters, such as evacuation planning and disaster relief distribution. When an evacuation network is built, strategic analyses are needed to support its operations. One of these is a vulnerability analysis to determine the weak points of an evacuation transportation network is one of those. This study aims to analyze the vulnerability of evacuation transportation networks constructed using the cell transmission model. Previous studies have considered all the possible scenarios to investigate weak points without using mathematical approaches. In this paper, we propose a bi-level optimization model to identify the fragile spots in a network. Due to the complexity of the problem, a genetic algorithm combined with linear programming is developed to find good solutions in a reasonable computational time. Solutions from the proposed algorithm are compared with optimal solutions.
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M3 - Article
AN - SCOPUS:85060674655
VL - 25
SP - 663
EP - 673
JO - International Journal of Industrial Engineering : Theory Applications and Practice
JF - International Journal of Industrial Engineering : Theory Applications and Practice
SN - 1072-4761
ER -