Track: Supply Chain Management

Abstract

The globalization of trade, urbanization, and the international division of the labor are the
main factors, that have led to an increasing need for transportation. In fact, with the opening
of different economic markets and the interdependence between countries through the supply
and demand of goods. Companies around the world are increasingly facing globalization
effects. Specifically, they must satisfy their customers and improve their performance by
optimizing each sub-process in supply chain process Vehicle routing problem  one of the important combinatorial problems for goods distribution as well as for
passengers’ transportation. Over time, many variants of this problem were studied and
several solving approaches are proposed to reach optimal solutions with exact methods or
near optimal ones using approximate algorithms (heuristic and metaheuristic) . Recently,
Machine learning techniques are explored to find the best solutions of VRP problem with high
performance .
In this review, we present the classical formulation of the vehicle routing problem as well as
its most prominent variants. We provide an overview of the most significant machine learning
concepts and techniques used in the literature for solving VRPs. To classify the papers, we
followed the research methodology proposed by Mayring and we finally discuss the findings,
analyze the results, and compare each machine learning techniques applied to this problem
with other techniques.