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Comparison of heuristic and machine learning algorithms for a multi-objective vehicle routing problem

The vehicle routing problem is an optimisation problem with a high computational complexity that can be solved using heuristics methods to achieve near-optimal solutions in a reasonable amount of time. The work done in this study aims to compare the execution time and distance of different routing engines when using VROOM, as well as evaluate different implementations of the k-means algorithm by looking at the rand- and adjusted rand index. The results show a difference in the distance and execution time depending on which routing engine is used and it is unclear if there is a difference in the k-means implementations. Investigating the cause behind the observed results would be interesting in future works.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:his-24041
Date January 2024
CreatorsArneson, Sebastian, Borgenstierna, Mattias
PublisherHögskolan i Skövde, Institutionen för informationsteknologi
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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