Heuristics Selection with ML in CP Optimizer

Abstract

IBM^® ILOG^® CP Optimizer (CPO) is a constraints solver that integrates multiple heuristics with the goal of handling a large diversity of combinatorial and scheduling problems while exposing a simple interface to users. CPO’s intent is to enable users to focus on problem modelling while automating the configuration of its optimization engine to solve the problem. For that purpose, CPO proposes an Auto search mode which implements a hard-coded logic to configure its search engine based on the runtime environment and some metrics computed on the input problem. This logic is the outcome of a mix of carefully designed rules and fine-tuning using experimental benchmarks. This paper explores the use of Machine Learning (ML) for the off-line configuration of CPO solver based on an analysis of problem instances. This data-driven effort has been motivated by the availability of a proprietary database of diverse benchmark problems that is used to evaluate and document CPO performance before each release. This work also addresses two methodological challenges: the ability of the trained predictive models to robustly generalize to the diverse set of problems that may be encountered in practice, and the integration of this new ML stage in the development workflow of the CPO product. Overall, this work resulted in a speedup improvement of about 14% (resp. 31%) on Combinatorial problems and about 5% (resp. 6%) on Scheduling problems when solving with 4 workers (resp. 8 workers), compared to the regular CPO solver.