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.

我们考虑在配送系统中设计同时具有成本效益和视觉吸引力的车辆路线的问题。在本文中，我们认为聚类，一种流行的数据挖掘任务，为视觉吸引力提供了一个很好的代理。我们的主张得到了双目标有能力车辆路线问题的提议的支持，其中，除了寻求旅行成本最小化之外，还优化了在不同路线中划分的客户上定义的聚类标准。该模型通过多目标进化算法求解以逼近其帕累托边界。我们通过计算实验表明，我们的模型能够表征具有低路由成本的车辆路由解决方案，同时根据文献中提出的视觉指标具有吸引力。