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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 洪一薰(I-Hsuan Ethan Hong) | |
dc.contributor.author | Kuan-Ting Lin | en |
dc.contributor.author | 林冠廷 | zh_TW |
dc.date.accessioned | 2021-06-17T04:25:15Z | - |
dc.date.available | 2023-08-16 | |
dc.date.copyright | 2018-08-16 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-14 | |
dc.identifier.citation | Antunes, C. H., Martins, A. G., and Brito, I. S. (2004). A multiple objective mixed integer linear programming model for power generation expansion planning. Energy, 29(4):613–627.
Bazrafshan, M., Chung, T., and Gildea, D. (2012). Tuning as linear regression. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 543–547. Association for Computational Linguistics. Burke, E. K., Cowling, P. I., and Keuthen, R. (2000). Effective heuristic and metaheuristic approaches to optimize component placement in printed circuit board assembly. In Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, volume 1, pages 301–308. IEEE. Chen, J. (1997). The study of dyeing condition for normal pressure dyeable polyester fiber. Journal of the China Textile Institute, 7(3):188–195. Choi, G. S., Wang, Z., and Dornfeld, D. (1991). Adaptive optimal control of machining process using neural networks. In Robotics and Automation, 1991. Proceedings., 1991 IEEE International Conference on, pages 1567–1572. IEEE. Cook, D. F., Ragsdale, C. T., and Major, R. (2000). Combining a neural network with a genetic algorithm for process parameter optimization. Engineering applications of artificial intelligence, 13(4):391–396. Dong, H., Liu, Y., Shen, Y., and Wang, X. (2016). Optimizing machining parameters of compound machining of inconel718. Procedia CIRP, 42:51–56. Grahne, G. and Zhu, J. (2005). Fast algorithms for frequent itemset mining using fp-trees. IEEE transactions on knowledge and data engineering, 17(10):1347–1362. Han, J., Cheng, H., Xin, D., and Yan, X. (2007). Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery, 15(1):55–86. IBM ILOG (2017). CPLEX Optimizer, version 12.8. Jasper, W. J., Kovacs, E. T., and Berkstresser IV, G. A. (1993). Using neural networks to predict dye concentrations in multiple-dye mixtures. Textile Research Journal, 63(9):545–551. Kansal, H., Singh, S., and Kumar, P. (2005). Parametric optimization of powder mixed electrical discharge machining by response surface methodology. Journal of materials processing technology, 169(3):427–436. Kocis, G. R. and Grossmann, I. E. (1988). Global optimization of nonconvex mixed-integer nonlinear programming (minlp) problems in process synthesis. Industrial & engineering chemistry research, 27(8):1407–1421. Oliveira, E. J., Honorio, L. M., Anzai, A. H., and Soares, T. X. (2014). Linear programming for optimum pid controller tuning. Applied Mathematics, 5(06):886. So, A. and Liang, B. (2009). Optimal placement and channel assignment of relay stations in heterogeneous wireless mesh networks by modified bender’s decomposition. Ad Hoc Networks, 7(1):118–135. Tapia-Ubeda, F. J., Miranda, P. A., and Macchi, M. (2018). A generalized benders decomposition based algorithm for an inventory location problem with stochastic inventory capacity constraints. European Journal of Operational Research, 267(3):806–817. Tarvin, D. A., Wood, R. K., and Newman, A. M. (2016). Benders decomposition: Solving binary master problems by enumeration. Operations Research Letters, 44(1):80–85. Wang, L., Gordon, M. D., and Zhu, J. (2006). Regularized least absolute deviations regression and an efficient algorithm for parameter tuning. In Data Mining, 2006. ICDM’06. Sixth International Conference on, pages 690–700. IEEE. Wang, X. and Bide, M. (1998). Factors affecting the levelness of dyeing in reused acid dyebaths for nylon. Textile Chemist & Colorist, 30(4). Yang, T., Lin, H.-C., and Chen, M.-L. (2006). Metamodeling approach in solving the machine parameters optimization problem using neural network and genetic algorithms: A case study. Robotics and Computer-Integrated Manufacturing, 22(4):322–331. Yang, W. p. and Tarng, Y. (1998). Design optimization of cutting parameters for turning operations based on the taguchi method. Journal of materials processing technology, 84(1-3):122–129. Yıldız, A. R. (2009). A novel particle swarm optimization approach for product design and manufacturing. The International Journal of Advanced Manufacturing Technology, 40(5-6):617. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70284 | - |
dc.description.abstract | 在許多產業的產品製程中,一般有著大量的機台影響成品品質的優劣,如半導體製造或紡織業等。因此,如何有效的調整機台參數以達到控制項的目標就成為了相當重要的議題,稱之為參數調整(parameter tuning)。實務上常以實驗設計(DOE, design of experiment)的方式找出機台參數與控制項之間的關係用於調機時的依據,並以混整數規劃(Mixed-Integer Linear Programming; MILP)求解最佳調機策略;然而,此類型的問題複雜度會隨著機台參數及控制項的數量上升而呈指數成長,僅憑實驗結果與過往經驗的決策仍會有許多潛在的因子沒有被考慮到。本研究先利用歷史資料進行分析,找出較具指標性的調機策略作為資料分類的依據後,根據不同類別的資料各自進行迴歸分析(regression analysis),並透過最佳化模型即時針對機台現況給予最佳的調機策略。 | zh_TW |
dc.description.abstract | There are generally a large number of machines that affect the quality of products in the manufacturing processes. Therefore, how to effectively tune the parameters of the machine to achieve the target of the control items has become a very important issue, called parameter tuning. In practice, the relationship between machine parameters and control items is often determined by the design of experiment. The parameter tuning is solved by Mixed-Integer Linear Programming (MILP). However, the complexity of this type of problem exponentially grows as the number of machine parameters and control items increases. This study first uses historical data to find out the tuning pattern for data classification. The regression analysis is performed on the basis of the classification results followed by an optimization model to determine tuning parameters. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T04:25:15Z (GMT). No. of bitstreams: 1 ntu-107-R05546016-1.pdf: 585603 bytes, checksum: 3be9c39b39a0acc79a0d6c448f8f8fae (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 第一章緒論1
第二章數學模型6 2.1 迴歸模型建立. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 大M 法用於調機模式判別及非線性轉換. . . . . . . . . . . . . . . . . 8 2.2.1 調機模式區間判別之限制式. . . . . . . . . . . . . . . . . . . . 8 2.2.2 非線性限制式的轉換. . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 模型符號定義. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.4 混整數規劃架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.1 目標函數. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.2 機台參數與控制項上下界之限制式. . . . . . . . . . . . . . . . 12 2.4.3 機台參數與控制項變化量之關係式. . . . . . . . . . . . . . . . 13 2.4.4 補正項判別限制式. . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5 非線性限制式轉換. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.5.1 絕對值函數. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.5.2 二元變數連乘項. . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5.3 整數變數與二元變數相乘項. . . . . . . . . . . . . . . . . . . . 15 第三章數值分析與比較18 3.1 套件介紹. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2 模擬環境參數設定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.1 固定參數設定. . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.2 隨機生成參數設定. . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.3 頻繁項目集挖掘. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.4 求解能力表現. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.5 模型計算時間. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 第四章結論與未來方向26 參考文獻27 | |
dc.language.iso | zh-TW | |
dc.title | 以迴歸模型與混整數規劃最佳化機台參數 | zh_TW |
dc.title | Optimal tool performance tuning through regression and mixed integer programming | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳文智(Wen-Chih Chen),藍俊宏 | |
dc.subject.keyword | 參數調整,迴歸分析,混整數規劃, | zh_TW |
dc.subject.keyword | parameter tuning,regression analysis,mixed-integer linear programming, | en |
dc.relation.page | 29 | |
dc.identifier.doi | 10.6342/NTU201803388 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-08-15 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
顯示於系所單位: | 工業工程學研究所 |
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