進階搜尋


下載電子全文  
系統識別號 U0026-0507201623153400
論文名稱(中文) 以整體趨勢擴散技術為核心之灰預測模型求解面板產業退返品還貨備料問題
論文名稱(英文) Solving the Stock Preparing Problem in Return Materials Authorization Process in TFT-LCD Industry with MTD-based Grey Model
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系碩士在職專班
系所名稱(英) Department of Industrial and Information Management (on the job class)
學年度 104
學期 2
出版年 105
研究生(中文) 王永泰
研究生(英文) Yung-Tai Wang
學號 R37011299
學位類別 碩士
語文別 中文
論文頁數 47頁
口試委員 指導教授-利德江
口試委員-蔡東亦
口試委員-葉俊吾
口試委員-林良憲
中文關鍵字 灰預測  整體趨勢擴散技術  退貨授權申請 
英文關鍵字 grey models  mega-trend-diffusion technique  return materials authorization 
學科別分類
中文摘要 在各大企業紛紛導入六倍標準差的觀念來控管品質的現今,理論上還是
會有最多達0.3%的不良品送至客戶端,此時異常品維修及還貨流程機制就會啟動。但產品種類繁多且生命週期極短,加上客戶還貨交期的限制、以及庫存控管的壓力,因此如何應用短期時間序列資料進行客戶還貨數量的預測以為庫存準備之參考是一項重要的議題。雖然灰預測模型GM(1,1)常被應用在此類的學習問題上,然其預測值的準確度仍可藉由背景值的選取而進行改善,而背景值是由參數 而決定。因此本研究藉由整體趨勢擴散技術(mega-trenddiffusion,MTD) 學習各期資料的落點資訊來取代 而提出一個改良的MTDGM(1,1)模型。在研究資料方面,本論文從國內某面板產業公司取得八筆資料,以及由UCI大學機械學習資料庫取得公開測試資料來進行效果驗證,實驗結果顯示MTDGM(1,1)確實較其他改良式灰預測模型有更加的預測準確度。
英文摘要 Although most enterprises have practiced Six Sigma Project for a while, the products that have about 0.3% defectives are still shipping to their customers. When defectives are
detected at customer side, the product returning processes will correspondingly activate. However, the operating costs rise because most enterprises have to prepare certain
amount of stock to return products in time to satisfy their customers. Accordingly, it becomes an important issue to find the breakeven point to both prevent the stock from being hoarded to futility, and to return products in time. The grey models are widely taken to make the next term predictor in short-term time series data; though, there still exists the possibility to make their predictions more precise. Therefore, this research reveals a new grey model to improve the accuracy of predictors by setting the coefficient sets of in traditional grey model. The technique this paper employs to find the sets is the mega-trend-diffusion, and the grey model thus named MTDGM(1,1). In the experiments, the examined dataset that contains eight product types was taken from the leading company in the TFT-LCD industry. The results compared with several improved grey models show that MTDGM(1,1) has more precise predictors.
論文目次 摘要 .......................I
誌謝 ..................... XVII
目錄 ..................... XVIII
表目錄 .................... XX
圖目錄 .................... XXI
第一章緒論 ................. 1
1.1 研究背景................ 1
1.2 研究動機................. 1
1.3 研究目的.................. 4
1.4 研究流程.................. 4
第二章文獻探討................. 6
2.1 需求預測.................. 6
2.2 現行預測方法............... 7
2.2.1 定性預測法............... 7
2.2.2 定量預測法............... 9
2.3 灰色系統理論............... 11
2.3.1 灰預測種類 .............. 12
2.3.2 傳統灰預測模型GM(1,1).... 13
2.3.3 灰預測模型的改良 ........ 15
2.4 適應性灰預測模型 .......... 20
2.4.1 趨勢潛力追蹤法說明 ...... 20
2.4.2 適應性灰預測模型說明 .... 21
2.5 小結 ..................... 24
第三章研究方法................. 25
3.1 立論基礎.................. 25
3.2 整體趨勢擴展技術 .......... 26
3.3 本研究方法................ 29
3.3.1 獲得各階段參數值 ........ 29
3.3.2 計算各階段背景值 ........ 30
3.3.3 本研究方法之執行步驟 .... 30
第四章實例驗證................. 32
4.1 實驗資料................... 32
4.1.1 個案公司資料說明 ......... 32
4.1.2 SCCTS資料說明 ........... 33
4.2 實驗方式................... 34
4.3 預測誤差評估指標 ........... 34
4.4 實驗結果................... 35
4.4.1 個案資料................. 35
4.4.2 SCCTS資料 ............... 41
第五章結論與建議................ 43
5.1 結論 ...................... 43
5.2 建議 ...................... 44
參考文獻 ....................... 45
參考文獻 【中文部分】
張哲榮,2007年,「適應性灰預測模型」,國立成功大學工業與資訊管理學系,碩士論文。
【英文部分】
Chang, C. J., Li, D. C., Huang, Y. H., & Chen, C. C. (2015). A novel gray forecasting model based on the box plot for small manufacturing data sets. Applied Mathematics and Computation, 265, 400-408.
Chang, S. C., Lai, H. C., & Yu, H. C., (2005). A variable P value rolling grey forecasting model for Taiwan semiconductor industry production.Technological Forecasting & Social Change, 72(5), 623-640.
Chang, S. C., Wu, J. H., & Lee, C. T., (1999). A study on the characteristics of (k) of grey prediction. The 4th National Conference on Grey Theory and Applications, Kaohsiung, Taiwan, 291-296, (in Chinese)
Chen, H. S., & Chang, W. C., (1998). A study of optimal grey model GM(1,1). Journal of Grey System, 1(2), 141-145. (in Chinese)
Chen, K. W., & Lai, C. J., (2001). Optimal fixed in for GM(1,1). The 6th National Conference on Grey Theory and Applications, Yunlin, Taiwan, A26-A31. (in Chinese)
Deng, J. L., (1982). Control Problems of Grey Systems. Systems and Control Letters, 1(5), 288-294.
Deng, J. L., (1987). Grey System Fundamental Method. Huazhong University of Science and Technology Press, Wuhan, China. (in Chinese)
He, Y., & Bao, Y. D., (1992). Grey-Markov forecasting model and its application.Systems Engineer-Theory & Practice, 12(4), 59-63. (in Chinese)
Hsin, J. Y., & Tsai, Y. P., (2000). The research of superposition method for value in grey forecasting. The 5th National Conference on Grey Theory and Applications, Taipei, Taiwan, 305-308. (in Chinese)
Hsu, C. C., & Chen, C. Y., (2003). A modified Grey forecasting model for long-term prediction. Journal of the Chinese Institute of Engineers, 26(3), 301-308.
Hsu, C. I., & Wen, Y. H., (1998). Improved grey prediction models for the transpacific air passenger market. Transportation Planning and Technology, 22(2),87-107.
Li, D. C., Wu, C. S., Tsai, T. I., & Lina, Y. S. (2007). Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge. Computers & Operations Research, 34(4), 966-982.
Li, D. C., & Yeh, C. W. (2008). A non-parametric learning algorithm for small manufacturing data sets. Expert Systems with Applications, 34(1), 391-398.Li, D. C., Yeh, C. W., & Chang, C. J. (2009). An improved grey-based approach for
early manufacturing data forecasting. Computers & Industrial Engineering,57(4), 1161-1167.
Li, Y. G., Li, Q. F., & Zhao, G. F., (1992). An improvement on Grey forecasting model. System Engineering, 10(6), 27-31. (in Chinese)Liu, S. F., Dang, Y. G., & Fang, Z. G., (2004). The Theory of Grey System and Its Applications. Science Press, Beijing, China. (in Chinese)
Lu, H. C., (1996). Universal GM(1,1) model based on data mapping concept. The Journal of Grey System, 8(4), 307-319.
Luo, D., Liu, S. F., & Dang, Y. G., (2003). The optimization of grey model GM(1,1). Engineering Science, 5(8), 50-53. (in Chinese)
Luo, E. X., Qian, X. S., & Li, R., (2006). Construction and empirical research of the variable parameter value rolling grey forecasting model. Journal of University of Shanghai for Science and Technology, 28(5), 465-468. (in Chinese)
Pan, C. L., Huang, Y. F., & Lin, G. , (2002). The study on algorithm of grey prediction with iterative method as basis. The 7th National Conference on Grey Theory and Applications, Tainan, Taiwan, I27-I32.
Tan, G.. J. (2000). The structure method and application of background value in grey system GM(1,1) model (Ⅰ). Systems Engineer-Theory & Practice, 20(4),98-103. (in Chinese)
Tien, T. L., & Chen, S.P., (1996). Residual correction method of Fourier series to GM(1,1) Model. The 1st National Conference on Grey Theory and Applications, Kaohsiung, Taiwan, 93-101. (in Chinese)
Wen, K. L., Huang, Y. F., Chen, F. S., Lee, Y. B., Lian, Z. F., & Lai, J. R., (2002).Grey Prediction. Chuan Hwa Book Press, Taipei, Taiwan. (in Chinese)
Zhou, P., Ang, B. W., & Poh K. L., (2006). A trigonometric grey prediction approach to forecasting electricity demand. Energy, 31(14), 2839-2847.
Xie, N.-m., & Liu, S.-f. (2009). Discrete grey forecasting model and its optimization. Applied Mathematical Modelling, 33(2), 1173-1186.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2017-06-27起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2017-06-28起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw