Fast Algorithms for the Capacitated Vehicle Routing Problem using Machine Learning Selection of Algorithm's Parameters

Roberto Javier Asín Achá, Olivier Goldschmidt, Dorit S. Hochbaum, Isaías I. Huerta

Abstract: We present machine learning algorithms for automatically determining algorithm’s parameters for solving the Capacitated Vehicle Routing Problem (CVRP) with unit demands. This is demonstrated here for the “sweep algorithm” which assigns customers to a truck, in a wedge area of a circle of parametrically selected radius around the depot, with demand up to its capacity. We compare the performance of several machine learning algorithms for the purpose of predicting this threshold radius parameter for which the sweep algorithm delivers the best, lowest value, solution. For the selected algorithm, KNN, that is used as an oracle for the automatic selection of the parameter, it is shown that the automatically configured sweep algorithm delivers better solutions than the “best” single parameter value algorithm. Furthermore, for the real worlds instances in the new benchmark introduced here, the sweep algorithm has better running times and better quality of solutions compared to that of curren