Solving a hard Cutting Stock Problem by machine learning and optimisation
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Accepted version
Date
2015-09
Authors
Prestwich, Steven D.
Fajemisin, Adejuyigbe O.
Climent, Laura
O'Sullivan, Barry
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Published Version
Abstract
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.
Description
Keywords
Cutting Stock Problem , Machine learning , Optimisation problems
Citation
Prestwich, S. D., Fajemisin, A. O., Climent, L. and O’Sullivan, B. (2015) 'Solving a Hard Cutting Stock Problem by Machine Learning and Optimisation'. Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, Lecture Notes in Computer Science, vol 9284, Cham: Springer International Publishing, pp. 335-347. doi: 10.1007/978-3-319-23528-8_21
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© Springer International Publishing Switzerland 2015. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-23528-8_21