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Solving a Hard Cutting Stock Problem by Machine Learning and Optimisation

Authors: 
Publication Type: 
Refereed Conference Meeting Proceeding
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.
Conference Name: 
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2015
Digital Object Identifer (DOI): 
10.1007/978-3-319-23528-8_21
Publication Date: 
07/09/2015
Conference Location: 
Portugal
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
No