abstract
- © 2018This paper describes a method for solving vehicle routing problems with time windows, based on selecting meta-heuristics via meta-learning. Although several meta-heuristics already exist that can obtain good overall results on some vehicle routing problem instances, none of them performs well in all cases. By defining a set of meta-features that appropriately characterize different routing problem instances and using a suitable classifier, our model can often correctly predict the best meta-heuristic for each instance. The main contributions of this paper are the definition of two meta-feature sets, one based on what we call ¿basic¿ instance properties and another based on the number of feasible solutions found by perturbative heuristics via a greedy process. We use a multilayer perceptron classifier, combined with a wrapper meta-feature selection method, to predict the most suitable meta-heuristic to apply to a given problem instance. Our experimental results show that the proposed method can significantly improve upon the overall performance of well-known meta-heuristics in the field. Therefore, this paper proposes to store, share and exploit in an off-line scheme the solutions obtained in instances of different scenarios such as the academy or industry, with the aim of predicting good solvers for new instances when necessary.