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While many real-world combinatorial problems can be advantageously modeled and solved using Constraint Programming, scalability remains a major issue in practice. Constraint models that accurately reflect the inherent structure of a problem, solvers that exploit the properties of this structure, and reformulation techniques that modify the problem encoding to reduce the cost of problem solving are typically used to overcome the complexity barrier. In this paper, we investigate such approachesdoi:10.1007/978-3-540-74970-7_14 dblp:conf/cp/BayerMCK07 fatcat:mtzdunin2napfbjirdxc5wwgcu