A provably efficient algorithm for dynamic storage allocation
Journal of Computer and System Sciences - 18th Annual ACM Symposium on Theory of Computing (STOC), May 28-30, 1986
Developing a simulated annealing algorithm for the cutting stock problem
Computers and Industrial Engineering
A genetic algorithm for a 2D industrial packing problem
Computers and Industrial Engineering
Comparison of meta-heuristic algorithms for clustering rectangles
Computers and Industrial Engineering
The irregular cutting-stock problem mdash; a new procedure for deriving the no-fit polygon
Computers and Operations Research
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Bin Packing with Adaptive Search
Proceedings of the 1st International Conference on Genetic Algorithms
A New Placement Heuristic for the Orthogonal Stock-Cutting Problem
Operations Research
A comprehensive and robust procedure for obtaining the nofit polygon using Minkowski sums
Computers and Operations Research
Computers and Operations Research
An optimisation model for airlift load planning: GALAHAD and the quest for the 'holy grail'
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
A hyper-heuristic approach to strip packing problems
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A squeaky wheel optimisation methodology for two-dimensional strip packing
Computers and Operations Research
A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics
IEEE Transactions on Evolutionary Computation
An Exact Algorithm for the Two-Dimensional Strip-Packing Problem
Operations Research
A Parallel Branch-and-Bound Approach to the Rectangular Guillotine Strip Cutting Problem
INFORMS Journal on Computing
Computers and Operations Research
A fast layer-based heuristic for non-guillotine strip packing
Expert Systems with Applications: An International Journal
A skyline-based heuristic for the 2D rectangular strip packing problem
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
An efficient deterministic heuristic for two-dimensional rectangular packing
Computers and Operations Research
Automating the packing heuristic design process with genetic programming
Evolutionary Computation
Data Structures for Higher-Dimensional Rectilinear Packing
INFORMS Journal on Computing
A simple randomized algorithm for two-dimensional strip packing
Computers and Operations Research
A Reinforced Tabu Search Approach for 2D Strip Packing
International Journal of Applied Metaheuristic Computing
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Expert Systems with Applications: An International Journal
INFORMS Journal on Computing
An effective shaking procedure for 2D and 3D strip packing problems
Computers and Operations Research
Genotype-phenotype heuristic approaches for a cutting stock problem with circular patterns
Engineering Applications of Artificial Intelligence
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The best-fit heuristic is a simple yet powerful one-pass approach for the two-dimensional rectangular 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 execution 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 10 new benchmark problems to encourage further research and greater comparison between current and future methods.