A Squeaky Wheel Optimisation Methodology for Two Dimensional Strip Packing Edmund K. Burke a , Matthew R. Hyde a,* , Graham Kendall a a The University of Nottingham, School of Computer Science, Jubilee Campus, Wollaton Road, Nottingham, UK Abstract The two dimensional strip packing problem occurs in industries such as metal, wood, glass, paper, and textiles. The problem involves cutting shapes from a larger stock sheet or roll of material, while minimising waste. This is a well studied problem for which many heuristic methodologies are available in the literature, ranging from the basic ‘one-pass’ best-fit heuristic, to the state of the art Reactive GRASP and SVC(SubKP) iterative procedures. The con- tribution of this paper is to present a much simpler but equally competitive iterative packing methodology based on squeaky wheel optimisation. After each complete packing (iteration), a penalty is applied to pieces that directly decreased the solution quality. These penalties inform the packing in the next iteration, so that the offending pieces are packed earlier. This method- ology is deterministic and very easy to implement, and can obtain some best results on benchmark instances from the literature. Keywords: Cutting Stock, Heuristics, Local Search, Artificial Intelligence 1. Introduction Optimisation problems involving cutting stock arise in many industries such as metal, wood, glass, paper, and textiles. These problems generally involve cutting stock sheets of material into smaller shapes, and a typical objective is to minimise the waste. These problems form part of a wider * Corresponding Author. Tel: +44 115 84 68376 Fax: +44 115 951 4254 Email address: mvh@cs.nott.ac.uk (Matthew R. Hyde) URL: http://www.cs.nott.ac.uk/~mvh/ (Matthew R. Hyde) Preprint submitted to Computers and Operations Research July 26, 2010