Authors:
Roberto Asín-Achá
1
;
Olivier Goldschmidt
2
;
Dorit S. Hochbaum
3
and
Isaías I. Huerta
1
Affiliations:
1
Department of Computer Science, Universidad de Concepción, Chile
;
2
Riverside County Office of Education, U.S.A.
;
3
Department of Industrial Engineering and Operations Research, University of California, Berkeley, U.S.A.
Keyword(s):
Vehicle Routing, Machine Learning, Model Selection.
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
t leading algorithms. Another contribution here is the introduction of the new CVRP real world data benchmark based on about a million customers locations in Los Angeles and about a million customers locations in New York city areas. This new benchmark includes a total of 46000 problem instances.
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