Abstract
The paper deals with the new algorithm development and comparison of three one-dimensional stock cutting algorithms regarding trim loss. Three possible types of problems used in this study are identified as easy, medium and hard. Approximate method is developed which enables a comparison of solutions of all three types of problems and of the other two stochastic methods. The other two algorithms employed here are Genetic Algorithms (GA) with Improved Bottom-Left (BL) and Simulated Annealing (SA) with Improved BL. Two examples of method implementation for comparison of three algorithms are presented. The approximate method produced the best solutions for easy and medium cutting problems. However, GA works very well in hard problems because of its global search ability.
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© 2005 Springer-Verlag Berlin Heidelberg
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Bingul, Z., Oysu, C. (2005). Comparison of Stochastic and Approximation Algorithms for One-Dimensional Cutting Problems. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_101
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DOI: https://doi.org/10.1007/11538059_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28226-6
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