Two−dimension Film Cutting by Quality Grade
Main Article Content
Abstract
The plastic film manufacturing industry: One of the manufacturing processes, including cutting the film to the size that according to customer requirements, due to a variety of customers, including width, length, grade and due date, etc. This research aims to 1) form a mathematical model that use the genetic algorithm that create the optimizing patterns. 2) Developing the cutting film programs which following the patterns speed performance that had developed by the generating pattern and the application speed in the context of the cutting blade changed. To find the guidelines of the research on Two−dimension Film Cutting by Quality Grade, the first step is to studies the genetic algorithm theory related research and education in the management of the cutting two−dimensional material as much as possible. The second step is to analyze the research in the context of the proposed research. The third step is to educate deeply in the research context. Step four is synthesizing the mathematical model. The fifth is testing the feasibility of the mathematical model that synthesis. Step six Programming and testing with the actually work. Step seven testing with the human computation. And the eighth compare and analyze the results from the management’s technology with the old ways that is used the human computing. The result on this research that is developed by the synthesized mathematical model decreased the calculating time when compare with the man is 51 times (5185%) and reduce blades changed time in the manufacturing 13 percent less on average.
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References
[2] M. Sugi, S. Yusuke, O. Tsuyoshi, I. Kazuyoshi, and O. Jun, “A solution for 2D rectangular cutting stock problems with 3−Stage guillotine−cutting constraint,” International Journal of Automation Technology, vol. 4, no. 5, pp. 461–468, September 2010.
[3] F. Chauny, R. Loulou, S. Sadones, and F. Soumis, “A two−phase heuristic for the two−dimensional cutting−stock problem,” Journal of the Operational Research Society, vol. 42, no. 1, pp 39–47, January 1991.
[4] H. M. Jahromi, R. Tavakkoli−Moghaddam, A. Makui, and A. Shamsi, “Solving an one−dimensional cutting stock problem by simulated annealing and tabu search,” Journal of Industrial Engineering International, pp. 1–8, December 2012,
[5] W. N. P. Rodrigo, W. B. Daundasekera, and A. A. I Perera, “Pattern generation for two−dimensional cutting stock problem with location,” Indian Journal of Computer Science and Engineering (IJCSE), vol. 3, no. 2, pp. 354–368, April–May 2012.
[6] P. E. Sweeney and R. W. Haessler, “One−dimensional cutting stock decisions for rolls with multiple quality grades,” European Journal of Operational Research, vol. 44, no. 2, pp. 244–231, January 1990.
[7] MB. Aryanezhad, N. Fakhim Hashemi, A. Makui, and H. Javanshir, “A simple approach to the two−dimensional guillotine cutting stock problem,” Journal of Industrial Engineering International, pp. 1–10, December 2012.
[8] P. C. Gilmore and R. E. Gomor, “Multistage Cutting Problems of Two and Mord Dimensions,” Operations Research, vol. 13, no. 1 , pp. 94–120, January–February 1965.
[9] H. Javanshir, S. Rezaei, S. S. Najar, and S. S. Ganji, “Two dimensional cutting stock management in fabric industries and optimizing the large object’s length,” International Journal of Research and Reviews in Applied Sciences, vol. 4, no. 3 , pp. 243–249, August 2010.
[10] M. M. Malik, J. H. Taplin, and M. Qiu, “Variants of the Cutting Stock Problem and The Solution Methods,” International Journal of Economics and Economics and Finance Studies, vol. 5, no. 2, pp. 45–54, 2013.
[11] G. Belov and G. Scheithauer, “The number of setups (different patterns) in one−dimensional stock cutting,” Technical Report MATH−NM−15−2003, Dresden University, September 23, 2003, pp. 274–296.
[12] R. W. Haessler and P. E. Sweeney, Cutting Stock Problems and Solution Procedures. USA: The University of Michigan, Ann Arbor, Mi. 1991, pp. 141–150.
[13] K.−H. Liang, X. Yao, C. Newton, and D. Hoffman, “A new evolutionary approach to cutting stock problems with and without contiguity,” Computers & Operations Research, vol. 29, no.12, 2002.
[14] S. Alexandre de Araujo, K. C. Poldi, and J. Smith, “A genetic algorithm for the one−dimensional cutting stock problem with setups,” Pesquisa Operacional, vol. 34, no.2, pp. 165–187, May–August 2014.
[15] R. Hinterding and L. Khan, “Genetic algorithms for cutting stock problems: with and without contiguity,” Springer−Verlag London, UK, Technical Report 40 COMP 12, August, 1994.
[16] V. Petridis, S. Kazarlis, and A. Bakirtzis, “Varying fitness functions in genetic algorithm constrained optimization: the cutting stock and unit commitment problems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 28, no. 5, pp. 629–640, October 1998.
[17] C. Reeves, “Hybrid genetic algorithms fior bin−packing and related problems,” Annals of Operations Research, vol. 63, no. 3, pp. 371–396, June 1996.
[18] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Boston, MA, USA,: Addison−Wesley Longman Publishing Co., Inc. 1989.
[19] J. HMA Meghdad, R. Tavakkoli−Moghaddam, A. Makui, and A. Shamsi, “Solving an one−dimensional cutting stock problem by simulated annealing and tabu search,” Journal of Industrial Engineering International, pp.1–8, December 2012.