Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems

Luca Accorsi; Andrea Lodi; Daniele Vigo

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arXiv · 2109.13983

Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems

Luca Accorsi·Andrea Lodi·Daniele Vigo

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Abstract

Despite the extensive research efforts and the remarkable results obtained on Vehicle Routing Problems (VRP) by using algorithms proposed by the Machine Learning community that are partially or entirely based on data-driven analysis, most of these approaches are still seldom employed by the Operations Research (OR) community. Among the possible causes, we believe, the different approach to the computational evaluation of the proposed methods may play a major role. With the current work, we want to highlight a number of challenges (and possible ways to handle them) arising during the computational studies of heuristic approaches to VRPs that, if appropriately addressed, may produce a computational study having the characteristics of those presented in OR papers, thus hopefully promoting the collaboration between the two communities.

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BibTeX RIS

Luca Accorsi, Andrea Lodi, Daniele Vigo. 2021-09-28. Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems. https://arxiv.org/abs/2109.13983

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