Rješavanje problema usmjeravanja vozila s heterogenim flotama korištenjem genetskog algoritma

master's thesis

• Title (english): Solving the Vehicle Routing Problem with heterogeneous fleets using Genetic Algorithm
• Author: Tin Jukić
• Mentor: Marko Đurasević (mentor)
• Committee member: Marko Đurasević (predsjednik povjerenstva)
• Committee member: Domagoj Jakobović (član povjerenstva)
• Committee member: Nikolina Frid (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2025-02-19, Croatia

Abstract

Problem usmjeravanja vozila (VRP) sve je popularniji problem u domeni strojnog učenja. Postoje brojne instance VRP-a, a jedna od njih je EVRP, koja sadrži plug-in hibridna i električna vozila. Opisan je EVRP problem te sva vozila koja se u njemu koriste. Dodatno, opisan je genetski algoritam (GA) i njegova konfiguracija koja se u ovom radu koristila. Prikazani su dobiveni rezultati te su izvučeni odgovarajući zaključci. Uz to, objašnjeno je zašto električna vozila nisu pogodna za svaku mapu te... More kada bi bilo bolje koristiti alternative. Less

Abstract (english)

Vehicle routing problem (VRP) is a more and more popular problem in machine learning domain. There are many instances of VRP, and one of them is EVRP, which contains plug-in hybrid and electric vehicles. EVRP problem is described, alongside all the vehicles that are used in it. Furthermore, genetic algorithm (GA) is also described with its configuration in this article. All results are shown and appropriate conclusions are drawn. Alongside that, here is described why electric vehicles are not... More suitable for all maps, and when the alternatives should be considered. Less

• Keywords: problem usmjeravanja vozila; problem usmjeravanja električnih vozila; vozila s motorom s unutarnjim izgaranjem; plug-in hibridna vozila; električna vozila; strojno učenje; genetski algoritam
• Keywords (english): vehicle routing problem; electric vehicle routing problem; internal combustion engine vehicles; plug-in hybrid vehicles; electric vehicles; machine learning; genetic algorithm
• Language: croatian
• URN:NBN: urn:nbn:hr:168:003022
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2025-03-12 11:08:36