Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory

This paper focuses on the discrete robustness optimization of emergency transportation network with the consideration of timeliness and decision behavior of decision-maker under the limited rationality. Based on a situation that the nearer to disaster area, the higher probability of time delay, pros...

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Bibliographic Details
Published in:Journal of advanced transportation 2019-01, Vol.2019 (2019), p.1-18
Main Authors: Yuan, Jia-shan, Wang, Qingrong, Zhu, Changfeng, Zhang, Zheng-kun
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Aversion
Clusters
Decision making
Decision theory
Disasters
Earthquakes
Emergency preparedness
Emergency vehicles
Genetic algorithms
Genetic analysis
Optimization
Planning
Preferences
Rationality
Risk aversion
Robustness
Scheduling
Transportation
Transportation industry
Transportation networks
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Summary:This paper focuses on the discrete robustness optimization of emergency transportation network with the consideration of timeliness and decision behavior of decision-maker under the limited rationality. Based on a situation that the nearer to disaster area, the higher probability of time delay, prospect theory is specially introduced to reflect the subjective decision behavior of decision-maker. Then, a discrete robustness optimization model is proposed with the purpose of the better timeliness and robustness. The model is based on the emergency transportation network with multistorage centers and multidisaster points. In order to obtain the optimal solution, an improved genetic algorithm is designed by introducing a bidirectional search strategy based on a newfangled path cluster to obtain specific paths that connect each storage centers and each disaster points. Finally, a case study is exhibited to demonstrate the reasonability of the model, theory, and algorithm. The result shows that the path cluster with the better timeliness and robustness can be well obtained by using the prospect theory and improved genetic algorithm. The analysis especially reveals that the robustness is correspondent to the risk aversion in prospect theory.
ISSN:0197-6729
2042-3195
DOI:10.1155/2019/2728073