PEGA-EV: A Data-Driven Policy Learning Method for Energy-Efficient Electric Vehicle Routing with Time Windows

PEGA-EV: A Data-Driven Policy Learning Method for Energy-Efficient Electric Vehicle Routing with Time Windows | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article PEGA-EV: A Data-Driven Policy Learning Method for Energy-Efficient Electric Vehicle Routing with Time Windows Abdelkader Kaddour, Lamri Sayad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9205311/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Constructive heuristics are widely used for the Electric Vehicle Routing Problem with Time Windows (EVRPTW) due to their low computational cost and transparent decision logic. Under hard customer time windows and battery-limited operation, however, manually designed priority rules often fail to generalise across heterogeneous instance structures. We propose a Policy--Evolving Genetic Algorithm for Electric Vehicle Routing (PEGA--EV) that learns a deterministic linear routing policy embedded within a greedy route-construction procedure. Candidate moves are ranked using a compact set of instance-normalised features capturing spatial proximity, time-window urgency, customer demand, and distance to the depot. An extended variant, PEGA--6, augments this representation with an explicit energy-risk feature that penalises moves leaving limited post-move reachability to the depot or charging stations. Feasibility is enforced by construction through strict time-window compliance and conservative safe-haven reachability to charging infrastructure throughout route building. Experiments on the full 92-instance Schneider benchmark show that PEGA--EV consistently constructs fully feasible solutions with competitive fleet size and total distance at millisecond-scale construction times. Reductions in fleet size and travelled distance also indicate improved operational efficiency, which is relevant for energy-aware and sustainable electric vehicle logistics systems. These results indicate that PEGA--EV provides a fast and reliable constructive baseline for EVRPTW, delivering competitive solution quality under strict energy and time-window feasibility, with consistent zero-shot transferability across heterogeneous instance families. Electric vehicle routing time windows data-driven optimization policy learning constructive heuristics charging constraints sustainable transportation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9205311","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614579803,"identity":"ed504331-fb4a-4541-9247-60677458c304","order_by":0,"name":"Abdelkader Kaddour","email":"data:image/png;base64,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","orcid":"","institution":"Université de M'Sila","correspondingAuthor":true,"prefix":"","firstName":"Abdelkader","middleName":"","lastName":"Kaddour","suffix":""},{"id":614579804,"identity":"2084d79f-c9bb-468d-9240-6781238b4ecd","order_by":1,"name":"Lamri Sayad","email":"","orcid":"","institution":"Université de M'Sila","correspondingAuthor":false,"prefix":"","firstName":"Lamri","middleName":"","lastName":"Sayad","suffix":""}],"badges":[],"createdAt":"2026-03-24 01:23:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9205311/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9205311/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109384936,"identity":"37b36518-2c7f-401a-9c54-ea5f86e065b2","added_by":"auto","created_at":"2026-05-16 20:54:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":425352,"visible":true,"origin":"","legend":"","description":"","filename":"PEAGAEV2C.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9205311/v1_covered_319bd378-5583-4dcb-875c-6de773b4f0c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PEGA-EV: A Data-Driven Policy Learning Method for Energy-Efficient Electric Vehicle Routing with Time Windows","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Electric vehicle routing, time windows, data-driven optimization, policy learning, constructive heuristics, charging constraints, sustainable transportation","lastPublishedDoi":"10.21203/rs.3.rs-9205311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9205311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Constructive heuristics are widely used for the Electric Vehicle Routing Problem with Time Windows (EVRPTW) due to their low computational cost and transparent decision logic. Under hard customer time windows and battery-limited operation, however, manually designed priority rules often fail to generalise across heterogeneous instance structures. We propose a Policy--Evolving Genetic Algorithm for Electric Vehicle Routing (PEGA--EV) that learns a deterministic linear routing policy embedded within a greedy route-construction procedure. Candidate moves are ranked using a compact set of instance-normalised features capturing spatial proximity, time-window urgency, customer demand, and distance to the depot. An extended variant, PEGA--6, augments this representation with an explicit energy-risk feature that penalises moves leaving limited post-move reachability to the depot or charging stations. Feasibility is enforced by construction through strict time-window compliance and conservative safe-haven reachability to charging infrastructure throughout route building. Experiments on the full 92-instance Schneider benchmark show that PEGA--EV consistently constructs fully feasible solutions with competitive fleet size and total distance at millisecond-scale construction times. Reductions in fleet size and travelled distance also indicate improved operational efficiency, which is relevant for energy-aware and sustainable electric vehicle logistics systems. 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