Solving a Hard Cutting Stock Problem by Machine Learning and Optimisation
Refereed Conference Meeting Proceeding
We are working with a company on a hard industrial optimisation problem: a version of the well-known Cutting Stock Problem in which a paper mill must cut rolls of paper following certain cutting patterns to meet customer demands. In our problem each roll to be cut may have a different size, the cutting patterns are semi-automated so that we have only indirect control over them via a list of continuous parameters called a request, and there are multiple mills each able to use only one request. We solve the problem using a combination of machine learning and optimisation techniques. First we approximate the distribution of cutting patterns via Monte Carlo simulation. Secondly we cover the distribution by applying a k-medoids algorithm. Thirdly we use the results to build an ILP model which is then solved.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2015
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National University of Ireland, Cork (UCC)
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