ITM Web Conf.
Volume 14, 2017The 12th International Conference Applied Mathematical Programming and Modelling – APMOD 2016
|Number of page(s)||8|
|Published online||08 November 2017|
Explaining the Results of an Optimization-Based Decision Support System – A Machine Learning Approach
Department of Operations and Decision Systems, Université Laval, Québec, Canada
2 Department of Computer Science and Software Engineering, Université Laval, Québec, Canada
3 IATE Joint Research Unit, INRA Montpellier, France
4 Tetra Tech QI inc., Québec, Canada
In this paper, we present work conducted in order to explain the results of a commercial software used for real-time decision support for the flow management of a combined wastewater network. This tool is deployed in many major cities and is used on a daily basis. We apply decision trees to build rules for classifying and interpreting the solutions of the optimization model. Our main goal is to build a classifier that would help a user understand why a proposed solution is good and why other solutions are worse. We demonstrate the feasibility of the approach to our industrial application by generating a large dataset of feasible solutions and classifying them as satisfactory or unsatisfactory based on whether the objective function is a certain percentage higher than the optimal (minimum) objective. We evaluate the performance of the learned classifier on unseen examples. Our results show that our approach is very promising according to reactions from analysts and potential users.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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