{"title":"考虑重载货车在农村公路上绕行行为的路政车辆路径优化","authors":"Jinyu Jiang, Zhongzhen Yang","doi":"10.5325/transportationj.60.4.0339","DOIUrl":null,"url":null,"abstract":"Abstract:In response to the increasingly frequent detour behavior of overloaded trucks attempting to avoid overload inspection and punishment, this article develops a bilevel programming model to optimize the routes of road administration vehicles by simulating the capture and anticapture interaction between administration vehicles and overloaded trucks. The upper-level model determines the optimal patrol routes of administration vehicles to maximize the number of overloaded trucks that can be captured. The lower-level model deduces the detour routes of overloaded trucks based on their circumvention behavior relative to administration vehicles. To solve the bilevel programming model, this article proposes a heuristic algorithm combining the ant colony algorithm and the labeling algorithm. To validate the proposed model and algorithm, this article uses actual rural highway data for Guiyang, China. The result proves the feasibility and superiority of the proposed programming model and algorithm. Compared to traditional overload management techniques, considering the detour behavior of overloaded trucks in optimizing the routing of road administration vehicles improves the effectiveness of overload management by up to 1.65 times.","PeriodicalId":46529,"journal":{"name":"Transportation Journal","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Routing Optimization for Road Administration Vehicles with Consideration of Overloaded Truck Detour Behavior on Rural Highways\",\"authors\":\"Jinyu Jiang, Zhongzhen Yang\",\"doi\":\"10.5325/transportationj.60.4.0339\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract:In response to the increasingly frequent detour behavior of overloaded trucks attempting to avoid overload inspection and punishment, this article develops a bilevel programming model to optimize the routes of road administration vehicles by simulating the capture and anticapture interaction between administration vehicles and overloaded trucks. The upper-level model determines the optimal patrol routes of administration vehicles to maximize the number of overloaded trucks that can be captured. The lower-level model deduces the detour routes of overloaded trucks based on their circumvention behavior relative to administration vehicles. To solve the bilevel programming model, this article proposes a heuristic algorithm combining the ant colony algorithm and the labeling algorithm. To validate the proposed model and algorithm, this article uses actual rural highway data for Guiyang, China. The result proves the feasibility and superiority of the proposed programming model and algorithm. Compared to traditional overload management techniques, considering the detour behavior of overloaded trucks in optimizing the routing of road administration vehicles improves the effectiveness of overload management by up to 1.65 times.\",\"PeriodicalId\":46529,\"journal\":{\"name\":\"Transportation Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2021-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.5325/transportationj.60.4.0339\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Journal","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.5325/transportationj.60.4.0339","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MANAGEMENT","Score":null,"Total":0}
Routing Optimization for Road Administration Vehicles with Consideration of Overloaded Truck Detour Behavior on Rural Highways
Abstract:In response to the increasingly frequent detour behavior of overloaded trucks attempting to avoid overload inspection and punishment, this article develops a bilevel programming model to optimize the routes of road administration vehicles by simulating the capture and anticapture interaction between administration vehicles and overloaded trucks. The upper-level model determines the optimal patrol routes of administration vehicles to maximize the number of overloaded trucks that can be captured. The lower-level model deduces the detour routes of overloaded trucks based on their circumvention behavior relative to administration vehicles. To solve the bilevel programming model, this article proposes a heuristic algorithm combining the ant colony algorithm and the labeling algorithm. To validate the proposed model and algorithm, this article uses actual rural highway data for Guiyang, China. The result proves the feasibility and superiority of the proposed programming model and algorithm. Compared to traditional overload management techniques, considering the detour behavior of overloaded trucks in optimizing the routing of road administration vehicles improves the effectiveness of overload management by up to 1.65 times.
期刊介绍:
Transportation Journal is devoted to the publication of articles that present new knowledge relating to all sectors of the supply chain/logistics/transportation field. These sectors include supply chain/logistics management strategies and techniques; carrier (transport firm) and contract logistics firm (3PL and 4PL) management strategies and techniques; transport economics; regulation, promotion, and other dimensions of public policy toward transport and logistics; and education.