A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A probabilistic analysis of multidimensional bin packing problems
STOC '84 Proceedings of the sixteenth annual ACM symposium on Theory of computing
IEEE Transactions on Evolutionary Computation
Computer Vision and Image Understanding
Collaboration Between Hyperheuristics to Solve Strip-Packing Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Computational Optimization and Applications
Analysis of Distributed Genetic Algorithms for Solving a Strip Packing Problem
Large-Scale Scientific Computing
An evolutionary hyperheuristic to solve strip-packing problems
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Hyperheuristic encoding scheme for multi-objective guillotine cutting problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
New fast heuristics for the 2d strip packing problem with guillotine constraint
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Heuristic for the rectangular strip packing problem with rotation of items
Computers and Operations Research
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In this paper, we undertake an empirical study which examines the effectiveness of eight simple strip packing heuristics on data sets of different sizes with various characteristics and known optima. We restrict this initial study to techniques that produce guillotine patterns (also known as slicing floor plans) which are important industrially. Our chosen heuristics are simple to code, have very fast execution times, and provide a good starting point for our research. In particular, we examine the performance of the eight heuristics as the problems become larger, and demonstrate the effectiveness of a preprocessing routine that rotates some of the rectangles by 90 degrees before the heuristics are applied. We compare the heuristic results to those obtained by using a good genetic algorithm (GA) that also produces guillotine patterns. Our findings suggest that the GA is better on problems of up to about 200 rectangles, but thereafter certain of the heuristics become increasingly effective as the problem size becomes larger, producing better results much more quickly than the GA.