A Pattern Combination Based Approach to Two-Dimensional Cutting Stock Problem

Wan, Jinming; Wu, Yadong; Dai, Hongwei

doi:10.1007/11539902_39

A Pattern Combination Based Approach to Two-Dimensional Cutting Stock Problem

Jinming Wan¹⁹,
Yadong Wu¹⁹ &
Hongwei Dai¹⁹

Conference paper

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3612))

Abstract

A new approach for dealing with a huge number of cutting pattern combinations encountered in two-dimensional Cutting Stock Problem (CSP) is described. Firstly, cutting patterns are produced according to a novel cutting method LF(Lease Fit) algorithm which can effectively cuts a sequence of small rectangular pieces from a big stock, heuristically maximizing the stock’s utilization ratio. Then Genetic Algorithm (GA) is applied to search for a near optimal solution which consists of many patterns namely a pattern combination. To evaluate the combination’s fitness, LP (Linear Programming) algorithm is used in polynomial time without bringing about much error. The performance and efficiency are justified by numerical experiments.

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References

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Authors and Affiliations

Shanghai Jiao Tong University, Shanghai, China
Jinming Wan, Yadong Wu & Hongwei Dai

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Jinming Wan
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Yadong Wu
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Hongwei Dai
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Editors and Affiliations

School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore
Lipo Wang
School of Software, Sun Yat-Sen University, 510275, Guangzhou, China
Ke Chen
School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore
Yew Soon Ong

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Wan, J., Wu, Y., Dai, H. (2005). A Pattern Combination Based Approach to Two-Dimensional Cutting Stock Problem. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_39

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DOI: https://doi.org/10.1007/11539902_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
eBook Packages: Computer ScienceComputer Science (R0)

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