Capturing waste collection planning expert knowledge in a fitness
  function through preference learning
Laura Fernández Díaz Miriam Fernández Díaz José Ramón Quevedo Elena Montañés
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 $98\%$.