贫信息情况下的应急救援路径选择模型

A model for emergency rescue path selection under poor information condition

为及时获取突发事件后的最优应急救援路径,首先运用Zadeh模糊算子量化路段行程速度的模糊性,引入随机动态变量表征路段行程时间的随机性,以此刻画具有模糊性与随机性的贫信息路网环境;然后结合随机一致性条件,构建该环境下的应急救援路径选择模型,再通过数学归纳法将其转化为鲁棒优化模型,并利用改进Dijkstra算法完成求解;最后用KAUFMAN路网验证模型的有效性。结果表明：基于2类不同的Zadeh模糊算子估算路段行程速度,其平均绝对误差为6. 271km/h;与传统方法相比,用鲁棒优化模型得到的应急救援路径行程时间更短;而且这种模型的算法复杂度为多项式形式,故可应用于大规模路网。

In order to obtain the best emergency rescue path on time when an emergency occurs,an emergency rescue path selection model was built according to the random consistency condition after using the Zadeh fuzzy operators to quantize the fuzziness of links＇ travel speed and introducing a random dynamic variable to characterize the randomness of link＇s travel time,which could depict a road network under poor information condition with fuzziness and randomness. The model was transformed into a robust optimization model by mathematical induction, which could be solved by the modified Dijkstra algorithm. The KAUFMAN road network was applied to validate model ＇ s effectiveness. The results show that the mean absolute error of links＇ travel speed based on 2 different fuzzy operators is 6. 271 km/h,that compared with traditional methods,a shorter time emergency rescue path can be obtained by using the robust optimal model,and that the model＇s algorithm complexity is expressed as a polynomial,which makes the model suitable for large gauge road networks.