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Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems (2020)


Kärkkäinen, Tommi; Rasku, Jussi (2020). Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems. In Diez, Pedro; Neittaanmäki, Pekka; Periaux, Jacques; Tuovinen, Tero; Pons-Prats, Jordi (Eds.) Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems, Computational Methods in Applied Sciences, 54. Cham: Springer, 77-102. DOI: 10.1007/978-3-030-37752-6_6


JYU authors or editors


Publication details

All authors or editors: Kärkkäinen, Tommi; Rasku, Jussi

Parent publication: Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems

Parent publication editors: Diez, Pedro; Neittaanmäki, Pekka; Periaux, Jacques; Tuovinen, Tero; Pons-Prats, Jordi

ISBN: 978-3-030-37751-9

eISBN: 978-3-030-37752-6

Journal or series: Computational Methods in Applied Sciences

ISSN: 1871-3033

Publication year: 2020

Number in series: 54

Pages range: 77-102

Number of pages in the book: 250

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: http://doi.org/10.1007/978-3-030-37752-6_6

Open Access: Publication channel is not openly available


Abstract

Vehicle Routing Problems (VRP) are computationally challenging, constrained optimization problems, which have central role in logistics management. Usually different solvers are being developed and applied for different kind of problems. However, if descriptive and general features could be extracted to describe such problems and their solution attempts, then one could apply data mining and machine learning methods in order to discover general knowledge on such problems. The aim then would be to improve understanding of the most important characteristics of VRPs from both efficient solution and utilization points of view. The purpose of this article is to address these challenges by proposing a novel feature analysis and knowledge discovery process for Capacitated Vehicle Routing problems (CVRP). Results of knowledge discovery allow us to draw interesting conclusions from relevant characteristics of CVRPs.


Keywords: logistics; routing; optimisation; data mining; machine learning

Free keywords: capacitated vehicle routing problems; feature extraction; knowledge discovery; robust statistics; autoencoder


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

Preliminary JUFO rating: 0


Last updated on 2020-09-07 at 23:09