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研究生:吳明修
研究生(外文):Ming-Shiu Wu
論文名稱:結合系統動態學與模糊算術-探討企業運作模式之研究
論文名稱(外文):Combining System Dynamics and Fuzzy Arithmetic-Enterprise Operations Model Applications
指導教授:張炳騰張炳騰引用關係
指導教授(外文):Ping-Teng Chang
學位類別:碩士
校院名稱:東海大學
系所名稱:工業工程與經營資訊學系
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:2004
畢業學年度:92
語文別:中文
論文頁數:94
中文關鍵詞:系統動態學模糊理論模糊算術α-cut模糊算術Weakest t-norm運算
外文關鍵詞:System dynamicsFuzzy setsFuzzy arithmeticα-cut fuzzy arithmeticWeakest t-norm operator
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本研究所提出的模糊系統動態(Fuzzy System Dynamics)是建立在系統動態學(System Dynamics)基礎之上,傳統系統動態學容易被批評的地方,是真實世界中的模型包含了許多不確定性因子,而系統動態學需要將這些因子以明確數值(Crisp number)表示才能進行運算。如果系統可以直接處理這些不確定因子,則系統將會更有效率。本研究使用模糊算術(fuzzy arithmetic)取代系統動態學中的明確數值計算方法,提供決策者更明確、多樣化的資訊。在我們的例子中可以觀察到模糊系統動態更快速反應顧客的訂單。提供產業實際應用的重要目標。
系統動態的複雜性在於各變數之間的關係交互影響,包含積量變數(Level variable)與率量變數(Rate variable)間關係之探討和積量變數與率量變數各自內在的關係性。率量變數之間具有線性獨立、線性相依、非線性獨立、非線性相依等四種特性,於本論文主要探討以模糊化觀點處理系統中線性獨立的率量變數與積量變數。並且探討α-cut、Weakest t-norm兩種模糊算術方法對於對稱、非對稱模糊數資料及不同模糊化輸入資料(fuzzier, medium-fuzzy, and less fuzzy)運算結果對系統的影響。

In this study a system dynamics analysis is presented based on the applications of fuzzy arithmetic. Traditional system dynamics may be observed that variables and parameters may belong to the uncertain factors. It is more beneficial if the system dynamics is extended to deal with also imprecise information or vague parameters and variables. The evaluations with fuzzy arithmetic operations may provide more insightful information regarding the uncertainties of the system to the decision makers. The three models are examined here in detail with two types of fuzzy arithmetic, the α-cut fuzzy arithmetic and Weakest t-norm operator. Symmetrical and non-symmetrical triangular-fuzzy-number inputs and fuzzier and less fuzzy inputs are examined and compared with the two types of fuzzy arithmetic with results provided.
These interactive relationships include those between the level and rate variables and functions of these rate and level variables. Conventionally, four types of relationships include the linear independent, linear dependent, nonlinear independent, and nonlinear dependent relationships. In this study, we should focus on the linear relationship between the rate and level variables and the linear dependent relationship between (or among) rate variables first and their influences on the system.

目錄
摘要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 研究流程與架構 2
第二章 文獻探討 4
2.1 系統動態學相關文獻 4
2.1.1 系統動態學簡介 4
2.1.2 系統動態學模型 4
2.2 模糊理論相關文獻 6
2.2.1 模糊理論簡介 6
2.2.2 模糊集合(fuzzy set) 6
2.2.3 模糊數與隸屬度函數 7
2.2.3.1 三角型隸屬度函數 7
2.2.3.2 梯型隸屬度函數 7
2.2.3.3 α-截集(α-cut)模糊算術 8
2.2.4 Weakest t-norm 模糊算術 10
2.2.5 解模糊化(Defuzzification) 12
2.3 目前相關研究 12
第三章 研究方法 15
3.1 模糊系統動態方程式(FUZZY SYSTEM DYNAMICS EQUATIONS) 15
3.2 實例操作:顧客、生產者、員工模型 16
3.2.1 案例1專有名詞對照表 18
3.2.2 模糊系統動態運算步驟 19
第四章 方法實作與評估 23
4.1 系統實作-案例1 顧客、生產者、員工模型 23
4.1.1 案例1模擬結果與分析 25
4.1.2 案例1模糊數與明確值模擬結果比較 36
4.1.3 非對稱(更模糊化、左傾、右傾)模糊數對模型的影響 38
4.1.4 案例1非對稱三角型模糊數值比較表 45
4.2 系統實作-案例2 生產銷售系統模型 49
4.2.1 案例2介紹 49
4.2.2 案例2公式與專有名詞對照表 51
4.2.3 案例2模擬結果與圖形分析 52
4.2.4 案例2模糊數與明確值模擬結果比較 60
4.3 系統實作-案例3 存貨模型 62
4.3.1 案例3介紹 62
4.3.2 案例3公式與專有名詞對照表 64
4.3.3 案例3模擬結果與圖形分析 66
4.3.4 案例3模糊數與明確值模擬結果比較 72
第五章 結論與建議 73
5.1研究總結 73
5.2後續研究建議 73
參考文獻 74
(附錄一)MODEL 1 CRISP程式 77
(附錄二)MODEL 1α-CUT三角型隸屬度函數程式 78
(附錄三)MODEL 1WEAKEST T-NORM三角型隸屬度函數程式 80
(附錄四)α-CUT與WEAKEST T-NORM加法、減法、乘法、除法 82

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