Fast Algorithms for the Capacitated Vehicle Routing Problem using Machine Learning Selection of Algorithm’s Parameters

Roberto Asín-Achá, Olivier Goldschmidt, Dorit S. Hochbaum, Isaías I. Huerta

2022

Abstract

We present machine learning algorithms for automatically determining algorithm’s parameters for solving the Capacitated Vehicle Routing Problem (CVRP) with unit demands. This is demonstrated here for the “sweep algorithm” which assigns customers to a truck, in a wedge area of a circle of parametrically selected radius around the depot, with demand up to its capacity. We compare the performance of several machine learning algorithms for the purpose of predicting this threshold radius parameter for which the sweep algorithm delivers the best, lowest value, solution. For the selected algorithm, KNN, that is used as an oracle for the automatic selection of the parameter, it is shown that the automatically configured sweep algorithm delivers better solutions than the “best” single parameter value algorithm. Furthermore, for the real worlds instances in the new benchmark introduced here, the sweep algorithm has better running times and better quality of solutions compared to that of current leading algorithms. Another contribution here is the introduction of the new CVRP real world data benchmark based on about a million customers locations in Los Angeles and about a million customers locations in New York city areas. This new benchmark includes a total of 46000 problem instances.

Paper Citation

in Harvard Style

Asín-Achá R., Goldschmidt O., Hochbaum D. and Huerta I. (2022). Fast Algorithms for the Capacitated Vehicle Routing Problem using Machine Learning Selection of Algorithm’s Parameters. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 29-39. DOI: 10.5220/0011405400003335

in Bibtex Style

@conference{kdir22,
author={Roberto Asín-Achá and Olivier Goldschmidt and Dorit S. Hochbaum and Isaías I. Huerta},
title={Fast Algorithms for the Capacitated Vehicle Routing Problem using Machine Learning Selection of Algorithm’s Parameters},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={29-39},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011405400003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - Fast Algorithms for the Capacitated Vehicle Routing Problem using Machine Learning Selection of Algorithm’s Parameters
SN - 978-989-758-614-9
AU - Asín-Achá R.
AU - Goldschmidt O.
AU - Hochbaum D.
AU - Huerta I.
PY - 2022
SP - 29
EP - 39
DO - 10.5220/0011405400003335
PB - SciTePress