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研究生:謝昆哲
研究生(外文):Kun-Che Hsieh
論文名稱:應用快速混亂基因演算法於計數型雙次抽樣驗收計畫之研究
論文名稱(外文):Applying Fast Messy Genetic Algorithm on the Design of Attribute Double Sampling Plan
指導教授:鄭道明鄭道明引用關係
指導教授(外文):Tao-ming Cheng
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:營建工程系碩士班
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:105
中文關鍵詞:快速混亂基因演算法計數型雙次抽樣驗收計畫
外文關鍵詞:fast messy genetic algorithmsattribute double sampling plan
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營建工程品質之檢驗端賴良好的驗收計畫,驗收計畫依其性質可分為計數型與計量型兩大類。計數型抽樣計畫因使用時無須繁雜之統計計算,檢驗時計算簡單,檢驗設備也較為簡易,適用於可採用非破壞檢驗材料的驗收,例如:磁磚、玻璃、燈管及隔間板等。
計數型抽樣驗收(Attribute Sampling Plans, ASP)依其檢驗程序與次數可分為單次與雙次抽樣檢驗形式等,一般而言,雙次抽樣比單次抽樣可節省總驗收成本,因此實務上應用雙次抽樣較單次抽樣多,不過,雙次抽樣驗收計畫設計因需先擬定允收品質水準(Acceptable Quality Level, AQL)與拒收品質水準(Rejectable Quality Level, RQL),並考慮承包商風險(α Risk)及業主風險(β Risk),之後求取符合通過操作特性曲線上(AQL, 1-α)和(RQL, β)兩個座標點的允收參數(n1, n2, c1, c2)。此外,允收參數又必須全部是非負值之整數,因此在求解上有其困難度,傳統上曾常用試誤法或啟發式法則來搜尋可能解,然而,使用試誤法或啟發式算法不僅費時且並不保證搜尋的解為最小抽樣樣本數。
本研究乃應用快速混亂基因演算法(Fast Messy Genetic Algorithms, fmGA),整合α與β風險誤差減至最小與最少的抽樣樣本量等最佳化目標,以求解計數型雙次抽樣驗收計畫(Attribute Double Sampling Plan, ADSP)。本研究以Microsoft Visual Basic 6.0工具語言撰寫應用程式,程式乃提供較彈性的抽樣計畫,使用者可選擇適當的機率分配(超幾何分配、二項分配、卜氏分配),並輸入業主與承包商雙方於合約內所擬定的AQL和RQL品質要求門檻高低與α和β欲承擔之風險要求,進行目標值的計算,搜尋出最佳適存值之允收參數(n1, n2, c1, c2)解,程式並會將其演算結果繪製成世代收斂圖,方便使用者判定最佳解收斂狀態,以供決策者參考使用。
An effective acceptance sampling plan can control the construction quality. There are two categories of acceptance sampling plan, attribute sampling plan (ASP) and variable sampling plan (VSP). The ASP does not need a complicate statistic calculation and relatively easy to be used. It is usually applied in the Nondestructive Testing Evaluation.
Depending on the number of samples to be taken from the lot and test procedure, ASP can be classified as two types: single sampling plan and double sampling plan. Generally, the double sampling plan can save more testing cost than single sampling plan. Thus, application of double sampling plan is more popular than single sampling plan in practice. However, the acceptable quality level (AQL), rejectable quality level (RQL), and producer’s risk (α) and consumer’s risk (β) have to be considered in the design of Attribute Double Sampling Plan (ADSP). The combination of the acceptance parameters (n1, n2, c1, c2) have to be found on the operation characteristics (OC) curve and fit the predefined (AQL, 1-α) and (RQL, β). In addition, all of the acceptance parameters should be nonnegative integers. Therefore, it is difficult to find an appropriate solution. Traditionally, the trial-and-error method and the heuristic rules are usually used to find a solution. However, these two methods are time-consuming and plans of minimum sample sizes can not be assured to be reached.
In this study, we use Fast Messy Genetic Algorithms (fmGA) as a research method. Objectives of lowering deviation of risk levels (α, β) and minimizing sample size are integrated to find the ADSP. Users can choose a proper probability distribution (hypergeometric distribution, binominal distribution, and Poisson distribution) first. Then, enter the value of AQL, RQL, risk levels (α, β) that both a producer and a customer agree in the contract. Finally, the computer program designed for this study can proceed and find the optimal acceptance parameters (n1, n2, c1, c2).
摘要 I
Abstract III
誌謝 V
目錄 VII
表目錄 XI
圖目錄 XIII
第一章、緒論 1
1.1、研究問題說明與目的 1
1.2、研究方法與流程 2
1.3、論文架構 5
第二章、文獻回顧 7
2.1、計數型抽樣驗收計畫 7
2.2、電腦抽樣檢驗系統 10
2.3、基因演算法改良運用之相關研究 11
2.4、小結 13
第三章、理論介紹 15
3.1、抽樣檢驗基本概念 15
3.1.1、基本名詞與符號 16
3.2、抽樣計畫之種類 18
3.3、抽樣檢驗形式 22
3.3.1、單次抽樣(Single sampling) 22
3.3.2、雙次抽樣(Double sampling) 23
3.4、計數型雙次抽樣驗收計畫理論與允收機率 25
3.4.1、超幾何分配(hypergeometric distribution) 25
3.4.2、二項分配(binomial distribution) 25
3.4.3、卜氏分配(Poisson distribution) 26
3.4.4、計數型雙次抽樣參數之驗收機率 27
3.5、操作特性曲線 30
3.5.1、OC曲線上之名詞定義 31
3.6、計數型雙次抽樣驗收計畫參數設計 32
3.7、快速混亂基因演算法 34
3.7.1、背景介紹 34
3.7.2、組織架構與過程 35
第四章、演算機制與程式介紹 45
4.1、演算機制說明 45
4.1.1、建立求解ADSP所需之各項抽樣參數值 47
4.1.2、設定fmGA演算機制所需參數 47
4.1.3、fmGA演算過程階段 47
4.1.4、記錄世代最佳ADSP允收參數(n1, n2, c1, c2)組合樣板 51
4.1.5、檢查演算終止條件與輸出最終結果 51
4.2、程式系統介紹 51
4.2.1、程式使用步驟 52
第五章、案例驗證與結果分析 59
5.1、敏感度分析 59
5.1.1、建立基本條件與參數設定 60
5.1.2、評估fmGA各項運算子變化時之敏感度分析 62
5.1.3、小結 70
5.2、案例應用說明 71
5.2.1、fmGA與傳統搜尋法分析比較(案例一) 71
5.2.2、fmGA與傳統搜尋法分析比較(案例二) 73
5.2.3、fmGA與sGA最佳化求解比較(以案例二為例) 75
5.2.4、fmGA與sGA最佳化求解比較(以驗收燈管案例為例) 80
第六章、結論與未來研究方向 83
6.1、結論 83
6.2、未來研究方向 85
參考文獻 87
附錄 93
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