This paper copes with the COGERSA waste collection process. Up to now, experts have been manually designed the process using a trial and error mechanism. This process is not globally optimized, since it has been progressively and locally built as council demands appear. Planning optimization algorithms usually solve it, but they need a fitness function to evaluate a route planning quality. The drawback is that even experts are not able to propose one in a straightforward way due to the complexity of the process. Hence, the goal of this paper is to build a fitness function though a preference framework, taking advantage of the available expert knowledge and expertise. Several key performance indicators together with preference judgments are carefully established according to the experts for learning a promising fitness function. Particularly, the additivity property of them makes the task be much more affordable, since it allows to work with routes rather than with route plannings. Besides, a feature selection analysis is performed over such indicators, since the experts suspect of a potential existing (but unknown) redundancy among them. The experiment results confirm this hypothesis, since the best $C-$index (98% against around 94%) is reached when 6 or 8 out of 21 indicators are taken. Particularly, truck load seems to be a highly promising key performance indicator, together to the travelled distance along non-main roads. A comparison with other existing approaches shows that the proposed method clearly outperforms them, since the $C-$index goes from 72% or 90% to 98%.

本文处理COGERSA废物收集过程。到目前为止，专家已经使用试错机制手动设计了该过程。此过程并未在全球范围内进行优化，因为它已根据理事会的要求逐步进行并在本地构建。规划优化算法通常可以解决该问题，但是它们需要适合性函数来评估路线规划质量。缺点是，由于过程的复杂性，即使是专家也无法直接提出建议。因此，本文的目标是通过偏好框架通过利用可用的专家知识和专长来构建适应度函数。专家们精心建立了几个关键绩效指标以及偏好判断，以学习有前途的适应度函数。尤其，它们的可加性使该任务更加经济实惠，因为它允许使用路线而不是路线计划。此外，由于专家怀疑这些指示符之间存在潜在的（但未知）冗余，因此对这些指示符进行了特征选择分析。实验结果证实了这一假设，因为$C-$当采用21项指标中的6项或8项时，指数达到98％对94％左右。特别是，卡车载荷与非主要道路的行驶距离一起，似乎是非常有前途的关键性能指标。与其他现有方法的比较表明，该建议方法明显优于它们，因为$C-$指数从72％或90％升至98％。