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dc.contributor.advisorBordin, Chiara
dc.contributor.advisorKampffmeyer, Michael
dc.contributor.authorFossum, Astrid
dc.date.accessioned2023-08-25T11:55:44Z
dc.date.available2023-08-25T11:55:44Z
dc.date.issued2023-06-01
dc.description.abstractEfficient routing optimization yields benefits that extend beyond mere financial gains. In this thesis, we present a methodology that utilizes a graph convolutional neural network to facilitate the development of energy-efficient waste collection routes. Our approach focuses on a Waste company in Tromsø, Remiks, and uses real-life datasets, ensuring practicability and ease of implementation. In particular, we extend the dpdp algorithm introduced by Kool et al. (2021) [1] to minimize fuel consumption and devise routes that account for the impact of elevation and real road distance traveled. Our findings shed light on the potential advantages and enhancements these optimized routes can offer Remiks, including improved effectiveness and cost savings. Additionally, we identify key areas for future research and development.en_US
dc.identifier.urihttps://hdl.handle.net/10037/30446
dc.language.isoengen_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDEOM-3901
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.subjectMachine Learningen_US
dc.subjectOperational Researchen_US
dc.subjectWaste managementen_US
dc.titleEfficient Fuel Consumption Minimization for Green Vehicle Routing Problems using a Hybrid Neural Network-Optimization Algorithmen_US
dc.typeMaster thesisen_US
dc.typeMastergradsoppgaveen_US


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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)