Efficient Fuel Consumption Minimization for Green Vehicle Routing Problems using a Hybrid Neural Network-Optimization Algorithm
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https://hdl.handle.net/10037/30446Dato
2023-06-01Type
Master thesisMastergradsoppgave
Forfatter
Fossum, AstridSammendrag
Efficient 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.
Forlag
UiT The Arctic University of NorwayUiT Norges arktiske universitet
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