Selecting fast algorithms for the capacitated vehicle routing problem with machine learning techniques

Abstract We present machine learning (ML) methods for automatically selecting a “best” performing fast algorithm for the capacitated vehicle routing problem (CVRP) with unit demands. Algorithm selection is to automatically choose among a portfolio of algorithms the one that is predicted to work best for a given problem instance, and algorithm configuration is to automatically select algorithm's parameters that are predicted to work best for a given problem instance. We present a framework incorporating both algorithm selection and configuration for a portfolio that includes the automatically configured “Sweep Algorithm,” the first generated feasible solution of the hybrid genetic search algorithm, and the Clarke and Wright algorithm. The automatically selected algorithm is shown here to deliver high‐quality feasible solutions within very small running times making it highly suitable for real‐time applications and for generating initial feasible solutions for global optimization methods for CVRP. These results bode well to the effectiveness of utilizing ML for improving combinatorial optimization methods.