Correspondence:
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Corresponding author: S. Mathieu, Quartier Polytech 1, Allée de la Découverte 10, B-4000 Liège, Belgium. Tel.: +32 4 366 2631; E-mail:sebastien.mathieu@ulg.ac.be
Abstract: To increase their competitiveness, many industrial companies monitor their production process, collecting large amount of measurements.
This paper describes a technique using this data to improve the performance of a monitored process.
In particular we wish to find a set of rules, i.e. intervals on a reduced number of parameters, for which an output value is maximized.
The model-free optimization problem to solve is to find a box, restricted on a limited amount of dimensions, with the maximum mean value of the included points.
This article compares a machine learning-based heuristic to the solution computed by a mixed-integer linear program on real-life databases from steel and glass manufacturing.
Computational results show that the heuristic obtains comparable solutions to the mixed integer linear approach.
However, the exact approach is computationally too expensive to tackle real life databases.
Results show that the restriction of five process parameters, on these databases, may improve the quality of the process by 50%.
Keywords: Data mining, integer programming, heuristics, industrial process, quality management
