{"title":"A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm","authors":"Xiao-Yun Jiang, Wen-Chao Chen, Yu-Tong Liu","doi":"10.46604/ijeti.2023.12612","DOIUrl":null,"url":null,"abstract":"The study aims to optimize the vehicle routing problem, considering infeasible routing, to minimize losses for the company. Firstly, a vehicle routing model with hard time windows and infeasible route constraints is established, considering both the minimization of total vehicle travel distance and the maximization of customer satisfaction. Subsequently, a Floyd-based improved genetic algorithm that incorporates local search is designed. Finally, the computational experiment demonstrates that compared with the classic genetic algorithm, the improved genetic algorithm reduced the average travel distance by 20.6% when focusing on travel distance and 18.4% when prioritizing customer satisfaction. In both scenarios, there was also a reduction of one in the average number of vehicles used. The proposed method effectively addresses the model introduced in this study, resulting in a reduction in total distance and an enhancement of customer satisfaction.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" 2","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Engineering and Technology Innovation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46604/ijeti.2023.12612","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The study aims to optimize the vehicle routing problem, considering infeasible routing, to minimize losses for the company. Firstly, a vehicle routing model with hard time windows and infeasible route constraints is established, considering both the minimization of total vehicle travel distance and the maximization of customer satisfaction. Subsequently, a Floyd-based improved genetic algorithm that incorporates local search is designed. Finally, the computational experiment demonstrates that compared with the classic genetic algorithm, the improved genetic algorithm reduced the average travel distance by 20.6% when focusing on travel distance and 18.4% when prioritizing customer satisfaction. In both scenarios, there was also a reduction of one in the average number of vehicles used. The proposed method effectively addresses the model introduced in this study, resulting in a reduction in total distance and an enhancement of customer satisfaction.
期刊介绍：
The IJETI journal focus on the field of engineering and technology Innovation. And it publishes original papers including but not limited to the following fields: Automation Engineering Civil Engineering Control Engineering Electric Engineering Electronic Engineering Green Technology Information Engineering Mechanical Engineering Material Engineering Mechatronics and Robotics Engineering Nanotechnology Optic Engineering Sport Science and Technology Innovation Management Other Engineering and Technology Related Topics.
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