Chemical hazardous material (Hazmat) transportation has become a very important safety issue to human society and the environment. Great attention has been drawn to reduce potential risks and incidents. In this paper, a new methodology is proposed for the dynamic routing optimization of the chemical Hazmat transportation, which includes four major stages: (i) information collection and preparation; (ii) modeling and solving individual and system routing models; (iii) reactive routing optimization under uncertainties; and (iv) trade-off study for potential shipping delays. A novel MILP model has been developed to determine the optimal shipping path with the minimal transportation risk. This model consists of two parts: the individual and system routing models, which are designed to explore the optimal shipping path for each shipping pair and all transportation tasks, respectively. When uncertainties occur, reactive routing optimization will be performed for handling the leftover transportation tasks. In particular, if some preset shipping time limits are violated due to severe uncertainties, optimal solutions subject to different allowable shipping time (AST) will be iteratively identified, so that the relation between AST and the corresponding transportation risk can be figured out. The efficacy of the developed model has been demonstrated by three case studies. Through the developed model, a reduction of 46% and 34% on system transportation risks of studied transportation tasks can be accomplished when dealing with uncertainties. The obtained relation can be used to trade off AST and the transportation risk, so that stakeholders can be advised on their decision-making for shipping path selection.

中文翻译：

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不确定条件下化学危险品运输的动态路径优化

化学有害物质（危险品）的运输已成为对人类社会和环境非常重要的安全问题。已经引起极大关注，以减少潜在的风险和事件。本文提出了一种针对化学危险品运输的动态路径优化的新方法，该方法包括四个主要阶段：（i）信息收集和准备；（ii）建模和求解个人和系统路由模型；（iii）不确定性下的反应式路由优化；（iv）对可能的运输延误进行权衡研究。已开发出一种新颖的MILP模型，以确定具有最小运输风险的最佳运输路径。该模型由两部分组成：个人路由模型和系统路由模型，旨在分别探索每个运输对和所有运输任务的最佳运输路径。当发生不确定性时，将执行反应式路线优化以处理剩余的运输任务。特别地，如果由于严重的不确定性而违反了一些预设的运输时间限制，则将迭代地确定受不同允许运输时间（AST）影响的最佳解决方案，从而可以找出AST与相应运输风险之间的关系。通过三个案例研究证明了所开发模型的有效性。通过开发的模型，当处理不确定性时，可以将研究的运输任务的系统运输风险分别降低46％和34％。