Abstract:

Hazardous materials transportation accidents have the characteristics of low probability and high consequences, and reducing the risk of accidents is the focus of research on hazardous materials transportation. The risk of hazardous materials transportation is accompanied by the uneven risk distribution in space. Previous studies have focused on the trade-off between the minimum total risk and the fairness of risk, but less consideration has been given to the transportation network in uncertain demand. Fluctuations in transportation demand may lead to increased risk. In order to solve the problem of hazardous materials network design considering risk fairness under uncertain demand, a double-layer mixed integer programming model for the design of transportation network with multiple origin-destination (OD) for hazardous materials is constructed. The problems are that the supervisor chooses the closed road section and the carrier chooses the optimal route, which are solved by the adaptive genetic algorithm and the local search algorithm respectively. An example analysis indicates that different demand scenarios, especially when some OD pairs are interrupted, produce different optimal network design schemes, and the genetic algorithm used is better than the local search algorithm when the running time is slightly longer.

Key words:

hazardous materials network design, risk equity, uncertain demand, bilevel programming, genetic algorithm

摘要：

危险品运输事故具有低概率、高后果的特征，降低事故风险是危险品运输研究的重点。危险品运输产生风险的同时伴随着风险在空间上的分布不均衡，此前研究关注总风险最小和风险公平的权衡，但较少考虑不确定需求下的运输网络。运输需求波动可能引起风险增加，为了解决不确定需求下考虑风险公平的危险品网络设计问题，构建了危险品多起点-终点(OD)对运输网络设计的双层混合整数规划模型，上层和下层问题分别为监管者选择关闭路段以及承运人选择最优路径，并通过自适应遗传算法和局部搜索算法进行求解。通过实例分析发现，不同需求情景特别是部分OD对中断时产生不同的最优网络设计方案，采用的遗传算法相比局部搜索算法运行时间稍长情况下获得的解更优。

关键词:

危险品网络设计, 风险公平, 不确定需求, 双层规划, 遗传算法