Despite the extensive research efforts and the promising results obtained by the ML community on Vehicle Routing Problems, most of the proposed techniques are still seldom employed by the OR community. With the current work, we highlight a number of challenges arising during the computational evaluation of heuristics for VRPs. The resulting guidelines aim at defining a common testing setup for the approaches designed by the two communities, thus promoting and strengthening the collaboration between them.
Accorsi L., Lodi A., Vigo D. (2022). Guidelines for the computational testing of machine learning approaches to vehicle routing problems. OPERATIONS RESEARCH LETTERS, 50(2), 229-234 [10.1016/j.orl.2022.01.018].
Guidelines for the computational testing of machine learning approaches to vehicle routing problems
Accorsi L.
Primo
Membro del Collaboration Group
;Lodi A.Secondo
Membro del Collaboration Group
;Vigo D.Ultimo
2022
Abstract
Despite the extensive research efforts and the promising results obtained by the ML community on Vehicle Routing Problems, most of the proposed techniques are still seldom employed by the OR community. With the current work, we highlight a number of challenges arising during the computational evaluation of heuristics for VRPs. The resulting guidelines aim at defining a common testing setup for the approaches designed by the two communities, thus promoting and strengthening the collaboration between them.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
