Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems
This work aims to bridge the gap between machine learning and operations research communities for vehicle routing problems, though it is incremental as it focuses on improving evaluation practices rather than introducing new methods.
The paper addresses the underutilization of machine learning approaches in vehicle routing problems by the operations research community, attributing it to differences in computational evaluation methods and proposing guidelines to align these evaluations with OR standards.
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.