Visual attractiveness in vehicle routing via bi-objective optimization

We consider the problem of designing vehicle routes in a distribution system that are at the same time cost-effective and visually attractive. In this paper we argue that clustering, a popular data mining task, provides a good proxy for visual attractiveness. Our claim is supported by the proposal of a bi-objective capacitated vehicle routing problem in which, in addition to seek for traveling cost minimization, optimizes clustering criteria defined over the customers partitioned in the different routes. The model is solved by a multi-objective evolutionary algorithm to approximate its Pareto frontier. We show, by means of computational experiments, that our model is able to characterize vehicle routing solutions with low routing costs which are, at the same time, attractive according to the visual metrics proposed in the literature.