A3 Book section, Chapters in research books
Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems (2020)
Kärkkäinen, T., & Rasku, J. (2020). Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems. In P. Diez, P. Neittaanmäki, J. Periaux, T. Tuovinen, & J. Pons-Prats (Eds.), Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems (pp. 77-102). Springer. Computational Methods in Applied Sciences, 54. https://doi.org/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: https://doi.org/10.1007/978-3-030-37752-6_6
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/73875
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
JUFO rating: 0
Parent publication with JYU authors: