(3.238.235.155) 您好!臺灣時間:2021/05/11 18:23
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

: 
twitterline
研究生:郭南奇
研究生(外文):Nan-Chi Kuo
論文名稱:以模糊方法評估不精確資料之單邊規格製程產出績效
論文名稱(外文):Using fuzzy approaches to evaluate the performance of one-sided processes with imprecise data
指導教授:吳建瑋吳建瑋引用關係
指導教授(外文):Chien-Wei Wu
學位類別:碩士
校院名稱:逢甲大學
系所名稱:工業工程與系統管理學研究所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:64
中文關鍵詞:模糊排序法模糊假設檢定不精確資料製程能力指標
外文關鍵詞:fuzzy ranking methodfuzzy hypothesis testingimprecise dataprocess capability indices
相關次數:
  • 被引用被引用:2
  • 點閱點閱:245
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:51
  • 收藏至我的研究室書目清單書目收藏:0
製程能力指標已廣泛地被運用來評估製程產出的績效是否滿足顧客的要求,文獻上製程能力分析的相關研究大多是假設量測資料為精確 (precise data) 的,但在實務上,由於測量誤差可能導致特性值無法被明確的量化,或產品具有不確定的品質特性等各種不明確的情況,可能使得傳統假設量測資料為明確時之製程能力評估方法衍生不確定性而產生誤判。因此,本研究考慮當製程產出為不精確時,運用模糊估計參數的方法,以單邊製程能力指標 Cpu 與 Cpl 為例,建構指標之模糊估計量並提出兩種不同的模糊檢定方法來評估單一製程產出之績效。此外,透過模擬的方式計算出此兩種檢定方法的型一誤差 (type I error) 與檢定力 (power)。再者,當面臨多個製程或供應商績效比較問題時,本研究利用模糊排序法 (fuzzy ranking approach) 來評估製程並排序比較出各個製程績效的優劣順序。最後,本文藉由模擬範例分析輔以佐證及說明,以提供在模糊環境下進行製程績效評估的參考。
Process capability indices have been widely applied to measure process performance. Most of traditional approaches for evaluating process capability are assumed that the underlying data are precise numbers, but it is much more realistic in general to consider observed values are imprecise numbers. Thus, this study takes into consideration of a certain degree of imprecision on the sample data and develops fuzzy estimators for two one-sided capability indices Cpu and Cpl by using Buckley’s approach with some extensions. Based on the fuzzy estimators of Cpu and Cpl, two fuzzy hypothesis testing methods are developed for assessing process performance. The error probability and the power of these two approaches are also investigated via simulation. In addition, this study also applies Yuan’s fuzzy ranking method to compare the performance of several available supplier processes and select a better supplier among them. Finally, a numerical example is presented to demonstrate the applicability of the proposed approach and provide simple guidelines for assessing process capability when the measurements of product quality are insufficiently precise.
誌謝 -------------------------------------------------I
中文摘要 ------------------------------------------------II
Abstract -----------------------------------------------III
圖目錄 ------------------------------------------------VI
表目錄 -----------------------------------------------VII
第一章 緒論---------------------------------------------1
1.1 研究動機與背景-----------------------------------1
1.2 研究目的-----------------------------------------3
1.3 研究架構-----------------------------------------3
第二章 文獻探討-----------------------------------------5
2.1 製程能力指標-------------------------------------5
2.2 單邊製程能力指標---------------------------------7
2.2.1 Cpu 與 Cpl 指標----------------------------------7
2.2.2 Cpu 與 Cpl 指標估計式----------------------------9
2.2.3 指標估計式之抽樣分配----------------------------10
2.3 模糊集合與模糊偏好關係--------------------------11
2.4 參數之模糊估計法--------------------------------12
2.5 製程能力之檢定----------------------------------13
第三章 研究方法----------------------------------------15
3.1 製程母體參數之模糊估計量------------------------15
3.2 指標 Cpu 與 Cpl 之模糊估計量--------------------16
3.3 alpha-cuts 之模糊檢定法-------------------------18
3.4 AR/AT 面積比值之模糊檢定法----------------------20
第四章 研究分析與結果----------------------------------22
4.1 程式模擬流程------------------------------------22
4.2 alpha-cuts 模糊檢定法之模擬結果-----------------25
4.2.1 型一誤差機率------------------------------------25
4.2.2 檢定力------------------------------------------29
4.2.3 範例分析----------------------------------------33
4.3 AR/AT 面積比值模糊檢定法之模擬結果--------------35
4.3.1 型一誤差機率------------------------------------35
4.3.2 檢定力------------------------------------------39
4.3.3 範例分析----------------------------------------43
第五章 供應商遴選問題暨範例分析------------------------45
5.1 模糊排序法--------------------------------------45
5.2 範例分析----------------------------------------49
第六章 結論與建議--------------------------------------51
參考文獻-------------------------------------------------52
附錄A 程式模擬之相關參數符號-----------------------------54
1. Boyles, R. A. (1991). The Taguchi capability index. Journal of Quality Technology, 23, 17-26.
2. Buckley, J. J. and Eslami, E. (2004). Uncertain probabilities II: the continuous case. Soft Computing, 8, 193-199.
3. Buckley, J. J. (2005) Fuzzy statistics: hypothesis testing. Soft Computing, 9, 512-518.
4. Chan, L. K., Cheng, S. W. and Spiring, F. A. (1988). A new measure of process capability: . Journal of Quality Technology, 20, 162-173.
5. Cheng, S. R., Hsu, B. M. and Shu, M. H. (2007). Fuzzy testing and selecting better processes performance. Industrial Management & Data Systems, 107(6), 862-881.
6. Chou, Y. M. and Owen, D. B. (1989). On the distributions of the estimated process capability indices. Communications in Statistics ﹣ Theory and Methods, 18(2), 4549-4560.
7. Filzmoser, P. and Viertl, R. (2004). Testing hypotheses with fuzzy data: the fuzzy p-value. Metrika, 59, 21-29.
8. Hsu, B. M. and Shu, M. H. (2008). Fuzzy inference to assess manufacturing process capability with imprecise data. European Journal of Operational Research, 186(2), 652-670.
9. Juran, J. M. (1974). Quality Control Handbook . McGraw-Hill, New York.
10. Kane, V. E. (1986). Process capability indices. Journal of Quality Technology, 18(1), 41-52.
11. Parchami, A. and Mashinchi, M. (2007). Fuzzy estimation for process capability indices. Information Sciences, 177, 1452-1462.
12. Parchami, A., Mashinchi, M. and Maleki, H. R. (2006). Fuzzy confidence interval for fuzzy process capability index. Journal of Intelligent & Fuzzy Systems, 17(3), 287-295.
13. Parchami, A., Mashinchi, M. and Partovi Nia, V. (2008). A consistent confidence interval for fuzzy capability index. Applied and Computational Mathematics, 7(1), 119-125.
14. Parchami, A., Mashinchi, M., Yavari, A. R. and Maleki, H. R. (2005). Process capability indices as fuzzy numbers. Austrian Journal of Statistics, 34(4), 391-402.
15. Pearn, W. L., Kotz, S. and Johnson, N. L. (1992). Distributional and inferential properties of process capability indices. Journal of Quality Technology, 24, 216-231.
16. Pearn, W. L. and Chen, K. S. (2002a). One-sided capability indices and : decision making with sample information. International Journal of Quality and Reliability Management, 19(3), 221-245.
17. Pearn, W. L. and Chen, K. S. (2002b). Testing process capability for one-sided specification limit with application to the voltage level translator. Microelectronics Reliability, 42, 1975-1983.
18. Taheri, S. M. (2003). Trends in fuzzy statistics. Austrian Journal of Statistics, 32(3), 239-257.
19. Wu, C. W. (2009). Decision-making in testing process performance with fuzzy data. European Journal of Operational Research, 193(2), 499-509.
20. Yongting, C. (1996). Fuzzy quality and analysis on fuzzy probability. Fuzzy Sets and Systems, 83, 283-290.
21. Yuan, Y. (1991). Criteria for evaluating fuzzy ranking methods. Fuzzy Sets and Systems, 43(2), 139-157.
22. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔