A Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock Cutting Problem Edmund K. Burke, Graham Kendall, Glenn Whitwell School of Computer Science & Information Technology, University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB, United Kingdom, {ekb@cs.nott.ac.uk, gxk@cs.nott.ac.uk, gxw@cs.nott.ac.uk} The best-fit heuristic is a simple yet powerful one-pass approach for the two-dimensional rectan- gular stock cutting problem. It had achieved the best published results on a wide range of benchmark problems until the development of the approaches described in this paper. Here, we illustrate how improvements in solution quality can be achieved by the hybridisation of the best- fit heuristic together with simulated annealing and the bottom-left-fill algorithm. We compare and contrast the new hybrid approach with other approaches from the literature in terms of exe- cution times and the quality of the solutions achieved. Using a range of standard benchmark problems from the literature we demonstrate how the new approach achieves significantly better results than previously published methods on almost all of the problem instances. In addition, we provide results on ten new benchmark problems to encourage further research and greater comparison between current and future methods. Key words: History: 1. Introduction Cutting and packing motivates many areas of operations research and arises, amongst others, in the paper, wood, glass and metal industries. There have been several articles that provide cate- gorised bibliographies and general overviews for the research undertaken under the term cutting and packing (Gilmore, 1966; Golden, 1976; Coffman, Garey and Johnson, 1984; Dyckhoff, 1990; Dowsland and Dowsland, 1992; Sweeney and Paternoster, 1992; Lodi, Martello and Mo- naci, 2002). Cutting and packing problems have many different formulations which are usually 1