A New Bottom-Left-Fill Heuristic Algorithm for the Two-Dimensional Irregular Packing Problem

Edmund Burke (), Robert Hellier (), Graham Kendall () and Glenn Whitwell ()
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Edmund Burke: School of Computer Science and Information Technology, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom
Robert Hellier: School of Computer Science and Information Technology, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom
Graham Kendall: School of Computer Science and Information Technology, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom
Glenn Whitwell: School of Computer Science and Information Technology, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom

Operations Research, 2006, vol. 54, issue 3, 587-601

Abstract: This paper presents a new heuristic algorithm for the two-dimensional irregular stock-cutting problem, which generates significantly better results than the previous state of the art on a wide range of established benchmark problems. The developed algorithm is able to pack shapes with a traditional line representation, and it can also pack shapes that incorporate circular arcs and holes. This in itself represents a significant improvement upon the state of the art. By utilising hill climbing and tabu local search methods, the proposed technique produces 25 new best solutions for 26 previously reported benchmark problems drawn from over 20 years of cutting and packing research. These solutions are obtained using reasonable time frames, the majority of problems being solved within five minutes. In addition to this, we also present 10 new benchmark problems, which involve both circular arcs and holes. These are provided because of a shortage of realistic industrial style benchmark problems within the literature and to encourage further research and greater comparison between this and future methods.

Keywords: search production/scheduling; cutting-stock/trim; production/scheduling; approximations/heuristic; computers/computer science; artificial intelligence (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (16)

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