{"title":"Electric Vehicle Routing With Recharging Stations: Trade-Offs in Last-Mile Delivery","authors":"Sinem Bozkurt Keser, İnci Sarıçiçek, Ahmet Yazıcı","doi":"10.1049/itr2.70052","DOIUrl":null,"url":null,"abstract":"<p>Last-mile logistics increasingly adopt electric vehicles to address environmental concerns and reduce operational costs. Unlike classical vehicle routing problems, it is essential to consider charging stations in the route planning for electric vehicles. This study aims to investigate the effect of different charging strategies on last-mile delivery optimisation. The adaptive large neighbourhood search (ALNS) algorithm is proposed to solve large-scale problems. The results of the proposed algorithm are compared with the results of the mathematical model in small-scale problems, and the algorithm's performance is proven. The proposed algorithm contributes to electric vehicle route planning by providing effective results in solving large-scale problems. The test problems are solved with three different charging strategies: full charging, partial charging, and partial charging between 20–80% state of charge (SoC). Solutions have been obtained for the objective functions of the minimising total distance, the minimizing total time, and the minimising total energy consumption. The results of the experiments show that the average charging time is the lowest when the total travel time is minimised, the highest values are reached when the total distance is minimised, and more balanced results are provided when the energy consumption is minimised. These findings help logistics companies to determine the most appropriate charging strategy in terms of operational efficiency and cost optimisation.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70052","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Intelligent Transport Systems","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/itr2.70052","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Last-mile logistics increasingly adopt electric vehicles to address environmental concerns and reduce operational costs. Unlike classical vehicle routing problems, it is essential to consider charging stations in the route planning for electric vehicles. This study aims to investigate the effect of different charging strategies on last-mile delivery optimisation. The adaptive large neighbourhood search (ALNS) algorithm is proposed to solve large-scale problems. The results of the proposed algorithm are compared with the results of the mathematical model in small-scale problems, and the algorithm's performance is proven. The proposed algorithm contributes to electric vehicle route planning by providing effective results in solving large-scale problems. The test problems are solved with three different charging strategies: full charging, partial charging, and partial charging between 20–80% state of charge (SoC). Solutions have been obtained for the objective functions of the minimising total distance, the minimizing total time, and the minimising total energy consumption. The results of the experiments show that the average charging time is the lowest when the total travel time is minimised, the highest values are reached when the total distance is minimised, and more balanced results are provided when the energy consumption is minimised. These findings help logistics companies to determine the most appropriate charging strategy in terms of operational efficiency and cost optimisation.
期刊介绍:
IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following:
Sustainable traffic solutions
Deployments with enabling technologies
Pervasive monitoring
Applications; demonstrations and evaluation
Economic and behavioural analyses of ITS services and scenario
Data Integration and analytics
Information collection and processing; image processing applications in ITS
ITS aspects of electric vehicles
Autonomous vehicles; connected vehicle systems;
In-vehicle ITS, safety and vulnerable road user aspects
Mobility as a service systems
Traffic management and control
Public transport systems technologies
Fleet and public transport logistics
Emergency and incident management
Demand management and electronic payment systems
Traffic related air pollution management
Policy and institutional issues
Interoperability, standards and architectures
Funding scenarios
Enforcement
Human machine interaction
Education, training and outreach
Current Special Issue Call for papers:
Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf
Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf
Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf