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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Solving a Hard Cutting Stock Problem by Machine Learning and Optimisation
verfasst von : Steven D. Prestwich, Adejuyigbe O. Fajemisin, Laura Climent, Barry O’Sullivan
Erschienen in: Machine Learning and Knowledge Discovery in Databases
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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.