Integrated OR/CP Optimization for Discrete Event Systems with Nonlinear Cost

Paper in proceeding, 2013

Optimization of a discrete event systems including (non)linear cost in local states is mainly solved either by heuristic search methods or mathematical programming. In this paper the second approach is further elaborated, including guarantees on both optimal performance and logical correctness. An integrated algorithm is developed utilizing both Operations Research (OR) and Constraint Programming (CP). The majority of integrated approaches have up till now focused on solving linear problems. In this paper we use our integrated algorithm to optimize discrete event systems with nonlinear cost and logical constraints. We present a straightforward method to incorporate OR functionality into an existing CP algorithm such that it can process nonlinear expressions, otherwise too complex for the CP algorithm to handle. Evaluation of the algorithm’s performance is done by comparison to that of state of the art Mixed Integer Nonlinear Programming (MINLP) methods. The benchmark shows that our integrated approach finds the optimal solution in roughly the same time as existing MINLP methods. However, when also proof of optimality is required, the integrated algorithm outperforms the best MINLP algorithm by roughly a factor of ten.