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Research on the water-ground multimodal transport emergency scheduling model and decision-making method considering the actual road network inundation situation.
Song, Yan; Sun, Baiqing; Han, Yunwu; Xing, Zhenming.
Afiliação
  • Song Y; School of Management, Harbin Institute of Technology, Harbin, China.
  • Sun B; School of Management, Harbin Institute of Technology, Harbin, China.
  • Han Y; Department of Automobile Engineering, Jiangsu Vocational College of Electronics and Information, Huai'an, China.
  • Xing Z; State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin, China.
Risk Anal ; 2024 Aug 21.
Article em En | MEDLINE | ID: mdl-39166706
ABSTRACT
As urbanization continues to accelerate worldwide, urban flooding is becoming increasingly destructive, making it important to improve emergency scheduling capabilities. Compared to other scheduling problems, the urban flood emergency rescue scheduling problem is more complicated. Considering the impact of a disaster on the road network passability, a single type of vehicle cannot complete all rescue tasks. A reasonable combination of multiple vehicle types for cooperative rescue can improve the efficiency of rescue tasks. This study focuses on the urban flood emergency rescue scheduling problem considering the actual road network inundation situation. First, the progress and shortcomings of related research are analyzed. Then, a four-level emergency transportation network based on the collaborative water-ground multimodal transport transshipment mode is established. It is shown that the transshipment points have random locations and quantities according to the actual inundation situation. Subsequently, an interactive model based on hierarchical optimization is constructed considering the travel length, travel time, and waiting time as hierarchical optimization objectives. Next, an improved A* algorithm based on the quantity of specific extension nodes is proposed, and a scheduling scheme decision-making algorithm is proposed based on the improved A* and greedy algorithms. Finally, the proposed decision-making algorithm is applied in a practical example for solving and comparative analysis, and the results show that the improved A* algorithm is faster and more accurate. The results also verify the effectiveness of the scheduling model and decision-making algorithm. Finally, a scheduling scheme with the shortest travel time for the proposed emergency scheduling problem is obtained.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article