School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, China.
Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems(No.2017TP1016), Changsha University of Science and Technology, Changsha, Hunan, China.
PLoS One. 2018 Jun 21;13(6):e0198931. doi: 10.1371/journal.pone.0198931. eCollection 2018.
Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.
危险物品运输路线优化是确保危险物品运输安全的基本步骤之一。如果在分配路线优化之前没有完成道路筛选，优化方案可能存在安全风险。针对危险物品运输的道路筛选问题，通过分析每条路网路段的 15 个属性数据，构建了基于遗传算法和 Levenberg-Marquardt 神经网络（GA-LM-NN）的危险物品运输道路筛选算法。针对单配送中心的危险物品运输问题，构建了具有可调稳健性的多目标鲁棒优化模型，以最小化运输风险和时间。根据模型的特点，设计了一种多目标遗传算法来解决该问题。该算法采用改进的策略来完成选择操作，应用部分匹配交叉移位和单正交交换方法来完成交叉和变异操作，并采用独占方法来构建帕累托最优解。研究表明，通过基于 GA-LM-NN 的提出的道路筛选算法可以快速找到危险物品运输道路集，而通过提出的多目标鲁棒优化模型和算法可以快速找到具有不同稳健性水平的分配路线帕累托最优解。
