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We consider an Integer Linear Programming (ILP) model for the problem that includes constraints deriving from real applications, such as stability and compression limits. In order to solve the ILP problem efficiently, we propose a method that exploits Machine Learning algorithms to classify predetermined layers of boxes, based on their \u201cimportance\u201d of being used for an ILP solution. This classification is used to heuristically limit the number of layers taken into account by the ILP solver. We demonstrate the effectiveness of our approach by comparing the ILP solution with and without the Machine Learning component. 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