Abstract
The performance of a constraint problem can often be improved by converting a subproblem into a single regular constraint. We describe a new approach to optimize constraint satisfaction (optimization) problems using constraint transformations from different kinds of global constraints to regular constraints, and their combination. Our transformation approach has two aims: 1. to remove redundancy originating from semantically overlapping constraints over shared variables and 2. to remove origins of backtracks in the search during the solution process. Based on the case study of the Warehouse Location Problem we show that our new approach yields a significant speed-up.
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Löffler, S., Liu, K., Hofstedt, P. (2020). Optimizing Constraint Satisfaction Problems by Regularization for the Sample Case of the Warehouse Location Problem. In: Schmid, U., Klügl, F., Wolter, D. (eds) KI 2020: Advances in Artificial Intelligence. KI 2020. Lecture Notes in Computer Science(), vol 12325. Springer, Cham. https://doi.org/10.1007/978-3-030-58285-2_26
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DOI: https://doi.org/10.1007/978-3-030-58285-2_26
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