摘要
效用最大化是应急救援决策中追求的首要目标。针对应急救援路径规划的决策特点和需求,对应急救援决策效用分析的关键因素和量化方法进行了探讨,提出了应急救援路径规划的二阶段优化模型。其中,首先引入DEA交叉评价模型对救援路段进行决策效用分析,在此基础上,设计了智能启发式算法用于路径规划。为避免过早陷入局部最优,设计了基于混沌扰动的改进蚁群系统优化算法,该算法可对信息素进行全局更新混沌扰动,可有效地提高算法的适应性、求解效率和求解质量。仿真实验表明该方法是可行的,可以更好地满足应急救援的决策需求。
Maximizing utility is the primary goal in emergency rescue decision.According to decision characteristics and demand of path planning in emergency rescue,the key factors and the quantitative methods are discussed,two-phase optimization model is also proposed.Aggressive cross-evaluation DEA(Data Envelopment Analysis)model is suggested to analyze the decision utilities of each path.On this basis,an intelligent inspired algorithm is designed for path planning.To avoid the remaining local optima of the ACS(Ant Colony System)algorithm,and in order to improve algorithm adaptability,computational efficiency and solution quality of the optimal solution,a chaos-based Ant Colony System(ACS)algorithm is proposed and realized.Simulation results show that the algorithm is feasible,can meet the demand of path planning in emergency rescue.
出处
《系统工程》
CSSCI
CSCD
北大核心
2015年第4期131-135,共5页
Systems Engineering
基金
国家自然科学基金资助项目(71301180)
重庆市科委自然科学基金资助项目(cstcjjA00021)
重庆市教委科技项目(KJ120427)
关键词
应急救援
路径规划
数据包络分析
混沌
蚁群系统算法
Emergency Rescue
Path Planning
Data Envelopment Analysis(DEA)
Chaos
Ant Colony System(ACS) Algorithm
