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

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Computation and Big Data for Transport

Part of the book series: Computational Methods in Applied Sciences ((COMPUTMETHODS,volume 54))

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.

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Acknowledgements

The authors would like to thank sincerely the main organizers of this research venue: Dr. Jacques Periaux and Dr. Tero Tuovinen.

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Authors and Affiliations

  1. Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland

    Tommi Kärkkäinen & Jussi Rasku

Authors
  1. Tommi Kärkkäinen
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  2. Jussi Rasku
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Corresponding author

Correspondence to Tommi Kärkkäinen .

Editor information

Editors and Affiliations

  1. International Center for Numerical Methods in Engineering, CIMNE, Universidad Politecnica Catalunya, Barcelona, Spain

    Pedro Diez

  2. Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland

    Pekka Neittaanmäki

  3. International Center for Numerical Methods in Engineering, CIMNE, Universitat Politècnica de Cataluny, Barcelona, Spain

    Jacques Periaux

  4. Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland

    Tero Tuovinen

  5. International Center for Numerical Methods in Engineering, CIMNE, Universidad Politecnica Catalunya, Barcelona, Spain

    Jordi Pons-Prats

Appendix

Appendix

See Tables 6.4 and 6.5.

Table 6.4 Feature extractors and feature details for the features related to customer and depot positions
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Table 6.5 Feature extractors and feature details for the features related to solving attempts, constraints, and feature computation times
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Kärkkäinen, T., Rasku, J. (2020). Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems. In: Diez, P., Neittaanmäki, P., Periaux, J., Tuovinen, T., Pons-Prats, J. (eds) Computation and Big Data for Transport. Computational Methods in Applied Sciences, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-030-37752-6_6

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