Acquiring Integer Programs from Data

Integer programming (IP) is widely used within operations research to model and solve complex combinatorial problems such as personnel rostering and assignment problems. Modelling such problems is difficult for non-experts and expensive when hiring domain experts to perform the modelling. For many tasks, however, examples of working solutions are readily available. We propose ARNOLD, an approach that partially automates the modelling step by learning an integer program from example solutions. Contrary to existing alternatives, ARNOLD natively handles multi-dimensional quantities and non-linear operations, which are at the core of IP problems, and it only requires examples of feasible solution. The main challenge is to efficiently explore the space of possible programs. Our approach pairs a general-to-specific traversal strategy with a nested lexicographic ordering in order to prune large portions of the space of candidate constraints while avoiding visiting the same candidate multiple times. Our empirical evaluation shows that ARNOLD can acquire models for a number of realistic benchmark problems

Authors

• Mohit Kumar
• Stefano Teso
• Luc De Raedt KU Leuven Örebro University

Keywords

• Constraints and SAT: Constraint Satisfaction
• Constraints and SAT: Constraints and Data Mining; Machine Learning
• Constraints and SAT: Modeling; Formulation