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Solving a Hard Cutting Stock Problem by Machine Learning and Optimisation

Prestwich, Steven D., Fajemisin, Adejuyigbe O., Climent, Laura and O'Sullivan, Barry (2015) Solving a Hard Cutting Stock Problem by Machine Learning and Optimisation. In: Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science (9284). Springer, Switzerland, pp. 335-347. ISBN 9783319235288

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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.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Staff Research and Publications
Depositing User: Caoimhe Ní Mhaicín
Date Deposited: 24 Sep 2018 08:18
Last Modified: 24 Sep 2018 08:18

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