Solving a Hard Cutting Stock Problem by Machine Learning and Optimisation

• Published in: Machine Learning and Knowledge Discovery in Databases pp. 335 - 347
• Main Authors: Prestwich, Steven D., Fajemisin, Adejuyigbe O., Climent, Laura, O’Sullivan, Barry
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2015
• Series: Lecture Notes in Computer Science
• ISBN: 3319235273; 9783319235271
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-23528-8_21

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.