scholarly journals A Risk-based Model of Evacuation Route Optimization under Fire

2018 ◽  
Vol 211 ◽  
pp. 365-371 ◽  
Author(s):  
Jing-jing Li ◽  
Hong-ya Zhu
Keyword(s):  
Route Optimization ◽  
Evacuation Route

Evacuation Route Optimization in the Plaza de la Mexicanidad, Using Humanitarian Logistics

2021 ◽  
pp. 41-56
Author(s):  
María Inés Borunda-Aguilar ◽  
Iván Juan Carlos Pérez-Olguín ◽  
Alberto Ochoa-Zezzatti ◽  
Erwin Adan Martinez-Gomez ◽  
José Alberto Hernández
Keyword(s):  
Humanitarian Logistics ◽  
Route Optimization ◽  
Evacuation Route

scholarly journals Simulation of Sports Venue Based on Ant Colony Algorithm and Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Zhang ◽  
Weibo Sun ◽  
Sang-Bing Tsai
Keyword(s):  
Partial Order ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Order Relation ◽  
Ant Colony ◽  
Route Optimization ◽  
Sports Facilities ◽  
Routing Method ◽  
Evacuation Plan ◽  
Evacuation Route

In order to improve the congestion of the evacuation plan and further improve the evacuation efficiency, this paper proposes the priority Pareto partial order relation and the vector pheromone routing method based on the priority Pareto partial order relation. Numerical experiments show that compared with the hierarchical multiobjective evacuation path optimization algorithm based on the hierarchical network, the fragmented multiobjective evacuation path optimization algorithm proposed in this paper effectively improves the evacuation efficiency of the evacuation plan and the convergence of the noninferior plan set. However, the congestion condition of the noninferior evacuation plan obtained by the fragmented multiobjective evacuation route optimization algorithm is worse than the congestion condition of the noninferior evacuation plan obtained by the hierarchical multiobjective evacuation route optimization algorithm. The multiple factors that affect the routing process considered in the probability transfer function used in the traditional ant colony algorithm routing method must be independent of each other. However, in actual route selection, multiple factors that affect route selection are not necessarily independent of each other. In order to fully consider the various factors that affect the routing, this paper adopts the vector pheromone routing method based on the traditional Pareto partial order relationship instead of the traditional ant colony algorithm. The model mainly improves the original pheromone distribution and volatilization coefficient of the ant colony, speeds up the convergence speed and accuracy of the algorithm, and obtains ideal candidate solutions. The method is applied to the location of sports facilities and has achieved good results. The experimental results show that the improved ant colony algorithm model designed in this paper is suitable for solving the problem of urban sports facilities location in large-scale space.

Evacuation route optimization architecture considering human factor

2017 ◽  
Vol 30 (1) ◽  
pp. 53-66 ◽  
Author(s):  
Marin Lujak ◽  
Sascha Ossowski
Keyword(s):  
Human Factor ◽  
Route Optimization ◽  
Evacuation Route

Route Optimization of Construction Machine by Deep Reinforcement Learning

2019 ◽  
Vol 139 (4) ◽  
pp. 401-408
Author(s):  
Shunya Tanabe ◽  
Zeyuan Sun ◽  
Masayuki Nakatani ◽  
Yutaka Uchimura
Keyword(s):  
Reinforcement Learning ◽  
Route Optimization ◽  
Construction Machine
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