Capturing waste collection planning expert knowledge in a fitness function through preference learning

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%.

Automated summary

This paper aims to improve the waste collection process by establishing a fitness function through a preference framework, utilizing expert knowledge. Experimental results suggest that selecting 6 or 8 key performance indicators can lead to optimal results, with truck load and distance traveled on non-main roads showing high potential as key indicators. The proposed method outperforms existing approaches, with the C-index improving from 72% or 90% to 98%.