A polynomial-size mixed integer linear programming model for the Precedence-Constrained Minimum-Cost Arborescence Problem with Waiting-Times was recently proposed in the literature, that uses a smaller number of variables and constraints compared to previously proposed polynomial-size models. In this work, we extend this model with constraint programming constructs to further enhance its performance. An extensive computational study support that modern constraint programming solvers is the best tool available at solving the models proposed. Several improvements to state-of-the-art results are finally reported.
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