论文部分内容阅读
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 pursued in emergency rescue decisions. According to the characteristics and demands of emergency rescue planning, this paper discusses the key factors and quantitative methods of emergency rescue decision-making effectiveness analysis and proposes a two-stage optimization model of emergency rescue planning. First of all, the DEA cross-evaluation model is introduced to analyze the decision-making utility of the rescue road segment. On this basis, an intelligent heuristic algorithm is designed for the path planning. In order to avoid premature falling into local optimum, an improved ant colony system optimization algorithm based on chaotic perturbation is designed. This algorithm can globally update the chaotic perturbation of pheromone, which can effectively improve the algorithm’s adaptability, solving efficiency and solving the quality. Simulation results show that this method is feasible and can better meet the emergency rescue decision-making needs.