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Swarm optimization algorithm for the electric vehicle routing problem

Gkatzolas Panagiotis

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URIhttp://purl.tuc.gr/dl/dias/A5538E6F-0A26-4038-9575-B7941D9ADEBD-
Identifierhttps://doi.org/10.26233/heallink.tuc.103802-
Languageen-
Extent58 pagesen
TitleSwarm optimization algorithm for the electric vehicle routing problemen
TitleΑλγόριθμος βελτιστοποίησης σμήνους σωματιδίων για το πρόβλημα δρομολόγησης οχημάτων με ηλεκτρικά οχήματαel
CreatorGkatzolas Panagiotisen
CreatorΓκατζολας Παναγιωτηςel
Contributor [Thesis Supervisor]Marinakis Ioannisen
Contributor [Thesis Supervisor]Μαρινακης Ιωαννηςel
Contributor [Committee Member]Matsatsinis Nikolaosen
Contributor [Committee Member]Ματσατσινης Νικολαοςel
Contributor [Committee Member]Marinaki Magdalinien
Contributor [Committee Member]Μαρινακη Μαγδαληνηel
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitTechnical University of Crete::School of Production Engineering and Managementen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Μηχανικών Παραγωγής και Διοίκησηςel
Content SummaryThe transition to electric mobility is one of the biggest technological and environmental challenges in transportation today. However, the limited battery autonomy of electric vehicles (EVs) and the need for efficient route planning makes it essential to optimize their routes so that energy consumption is minimized and operational costs are reduced. In this study, we examine the Electric Vehicle Routing Problem (E-VRP), an extension of the classic Vehicle Routing Problem (VRP), which considers additional constraints such as energy consumption, charging station availability, and vehicle capacity. Our goal is to develop an algorithm that will compute optimal routes for a fleet of electric vehicles while minimizing both travel distance and energy consumption. To solve this problem, we use the Particle Swarm Optimization (PSO) algorithm, which is inspired by the collective movement of particle swarms in nature. PSO is a widely used optimization algorithm because it does not require overly complex mathematical calculations and can find high-quality solutions in a relatively short time. Moreover, it converges quickly and retains information from previous high-performing solutions, improving its search efficiency in routing problems. In our study, we adapt the PSO to consider key factors such as: • The total distance traveled by EVs. • Energy consumption, which depends on vehicle load and route characteristics. • Charging station availability and congestion levels. • Delivery time constraints. To evaluate our method, we will implement the PSO and we will test it on benchmark instances from the literature. We will assess the quality of the solutions and computational time, and the results will indicate if the PSO can provide efficient solutions for the E-VRP, optimizing routing while keeping the computational cost relatively low. en
Type of ItemΔιπλωματική Εργασίαel
Type of ItemDiploma Worken
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2025-07-11-
Date of Publication2025-
SubjectE-CVRPen
SubjectElectric Capacitated Vehicle Routing Problemen
SubjectParticle swarm optimization algorithmen
SubjectPSOen
Bibliographic CitationPanagiotis Gkatzolas, "Swarm optimization algorithm for the electric vehicle routing problem", Diploma Work, School of Production Engineering and Management, Technical University of Crete, Chania, Greece, 2025en
Bibliographic CitationΠαναγιώτης Γκατζόλας, "Αλγόριθμος βελτιστοποίησης σμήνους σωματιδίων για το πρόβλημα δρομολόγησης οχημάτων με ηλεκτρικά οχήματα", Διπλωματική Εργασία, Σχολή Μηχανικών Παραγωγής και Διοίκησης, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2025el

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