Explaining the Results of an Optimization-Based Decision Support System – A Machine Learning Approach

Michael Morin 1 Rallou Thomopoulos 2, 3 Irène Abi-Zeid 4 Maxime Léger 4 François Grondin 5 Martin Pleau 5
1 IFT - Département d'Informatique - Université Laval
2 UMR IATE - Ingénierie des Agro-polymères et Technologies Émergentes
3 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
4 OSD - Département Opérations et Systèmes de Décision - Université Laval
5 Tetra Tech QI inc.
Abstract : 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 and sanitary wastewater system. 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 to 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 not based on whether the objective function is a certain percentage higher than the optimal 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.
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Michael Morin, Rallou Thomopoulos, Irène Abi-Zeid, Maxime Léger, François Grondin, et al.. Explaining the Results of an Optimization-Based Decision Support System – A Machine Learning Approach. APMOD: APplied mathematical programming and MODelling, Jun 2016, Brno, Czech Republic. ⟨lirmm-01382346v2⟩

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