Capturing waste collection planning expert knowledge in a fitness function through preference learning. (March 2021)

• Record Type: Journal Article
• Authors: Fdez-Díaz, Laura; Fdez-Díaz, Míriam; Quevedo, José Ramón; Montañés, Elena
• Is Part Of: Engineering applications of artificial intelligence. Volume 99(2021)
• Publication Date: 2021-03
• Subjects: Machine learning -- KPI -- Classification -- Preferences -- Route; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.104113
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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%. Highlights: A fitness function to evaluate a waste collection route planning. Key performance indicators for a waste collection route planning. Feature selection of key performance indicators for a waste collection route planning.