Hyperheuristic encoding scheme for multi-objective guillotine cutting problems

Authors:
Jesica de Armas;Gara Miranda;Coromoto León
Affiliations:
University of La Laguna, La Laguna, Spain;University of La Laguna, La Laguna, Spain;University of La Laguna, La Laguna, Spain
Venue:
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Year:
2011

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Abstract

Most research on Strip Packing and Cutting Stock problems are focused on single-objective formulations of the problems. However, in this work we deal with more general and practical variants of the problems, which not only seeks to optimise the usage of the raw material, but also the overall production process.The problems target the cutting of a large rectangle in a set of smaller rectangles using orthogonal guillotine cuts. Common approaches are based in the minimisation of the strip length required to cut the whole set of demanded pieces (for strip problems) and in the maximisation of the total profit obtained from the available surface (for cutting stock problems). In this work we also deal with an extra objective which seeks to minimise the number of cuts involved in the cutting process, thus maximising the efficiency of the global production process. In order to obtain solutions to these problems, we have applied some of the most-known multi-objective evolutionary algorithms, since they have shown a promising behaviour when tackling multi-objective real-world problems. We have designed and implemented hyperheuristic-based encodings as an alternative to combine heuristics in such a way that a heuristic's strengths make up for the drawbacks of another.