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The authors model the safety stock placement problem in general acyclic supply chain networks as a project scheduling problem, for which the Constraint Programming (CP) techniques are both effective and efficient in finding high quality solutions. They further integrate CP with a Genetic Algorithm (GA), which improves the CP solution quality significantly. The performance of their hybrid CP-GA algorithm is evaluated on randomly generated test instances. CP-GA is able to find optimal solutions to small problems in fractions of a second, and near optimal solutions of about 5% optimality gap to medium size problems in several minutes on average.