Abstract: Aiming at the route optimization problem of non-fully loaded hazardous material transportation vehicles in uncertain environment, the uncertain factors in the transportation process are described by fuzzy variables, and considering the impact of cargo volume change on risk assessment, a risk assessment model based on dynamic cargo volume is established. The optimization objectives are to minimize the total risk of transportation, the total travel of vehicles and the number of vehicles used, taking into account the time window based on the constraints of accident probability and load capacity, a multi-objective optimization model of hazardous material transportation vehicle route in uncertain environment is constructed. Combining NSGA-II algorithm with LNS algorithm, a hybrid NSGA-II algorithm solution model is designed. The results show that the hybrid NSGA-II algorithm can obtain the Pareto solution set with uniform spatial distribution and good convergence, and different transportation participants can choose the corresponding distribution scheme in the solution set according to their own preferences. The optimal total risk, total journey and number of vehicles obtained by the algorithm are 11.5%, 1.0% and 14.3% better than NSGA-II algorithm respectively. The search performance and solution accuracy of the algorithm are significantly improved.

Key words: transportation of dangerous goods, uncertain environment, dynamic load capacity, hybrid NSGA-II algorithm, path optimization

摘要： 针对非满载危险品运输车辆路径优化问题，通过模糊变量刻画运输过程中的人口密度、行驶速度与运输时间以及客户需求量等方面的不确定因素，考虑载货量变化对风险评估的影响，建立基于动态载货量的风险评估模型，以运输总风险、车辆总行程、车辆使用数最小为优化目标，同时兼顾时间窗、事故概率、载货量等约束构建了不确定环境下的危险品运输车辆路径多目标优化模型。将NSGA-II算法与LNS算法相结合，设计混合NSGA-II算法求解模型。结果表明，混合NSGA-II算法可以获得空间分布均匀且收敛性较好的Pareto解集，不同运输参与者可根据自身偏好在解集中选择相应的配送方案；该算法得到的最优总风险、总行程及车辆使用数目分别比NSGA-II算法优化了11.5%、1.0%和14.3%，算法搜索性能和求解精度明显提高。

关键词: 危险品运输, 不确定环境, 动态载货量, 混合NSGA-II算法, 路径优化