摘要
由于危险品在运输过程中存在极大的危害性,为了降低危险品运输风险,政府可以通过对不同路段设置不同的限速区间来引导危险品运输车辆的路径选择,从而导致不同的运输网络总风险和鲁棒成本。首先基于车辆限速区间的方法,构建了危险品运输网络优化的双层规划模型,上层规划以最大运输网络总风险值最小化为目标,下层规划以危险品运输企业的鲁棒成本最小化为目标;然后,设计了粒子群优化算法求解了该模型;最后,通过两个算例验证了模型和算法的有效性。计算结果表明政府部门运用车辆限速区间的方法不仅能够非常有效地降低危险品运输网络总风险,而且更具有鲁棒性和现实可操作性。
Due to the great harm of hazardous materials(hazmat)in the transportation process,in order to reduce risk of hazmat transportation,the government can guide the route selection of the hazmat vehicles by imposing different speed limit interval on different road links,which eventually results in the different total risk and robust cost of the hazmat transportation network.Based on the approach of vehicle speed limit interval,this paper firstly constructs a bi-level programming model for the hazmat transportation network optimization,in which the upper level aims to minimize the maximum total risk of the transportation network,and the lower level focuses on minimizing the robust cost of the hazmat carriers.Then,the model is solved by particle swarm optimization.Finally,two numerical experiments are employed to verify the effectiveness of the model and algorithm.The results show that the government can not only effectively reduce the total risk of the hazmat transportation network by the method of vehicle speed limit interval,but also be more robust and realistic.
作者
王伟
张宏刚
丁黎黎
张文思
高歌
张辉
WANG Wei;ZHANG Hong-gang;DING Li-li;ZHANG Wen-si;GAO Ge;ZHANG Hui(School of Economics, Ocean University of China, Qingdao 266100,China;School of Transportation, Southeast University, Nanjing 211100, China;School of Transportation, Shandong University of Science and Technology, Qingdao 266590, China;School of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China)
出处
《运筹与管理》
CSSCI
CSCD
北大核心
2021年第4期128-134,共7页
Operations Research and Management Science
基金
国家自然科学基金资助项目(71701189,71973132,71801144)
教育部人文社会科学研究青年基金项目(17YJCZH177)
山东省自然科学基金资助项目(ZR2017BG001)。
关键词
公路运输
车辆限速区间
危险品运输
双层规划
粒子群优化算法
highway transportation
vehicle speed limit interval
hazardous materials transportation
bi-level programming
particle swarm optimization