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研究生:李永祥
研究生(外文):Yung-Hsiang Lee
論文名稱:應用六標準差手法結合基因演算法探討3C產品工程變更之研究
論文名稱(外文):The Research of Applying Six Sigma methods with Genetic Algorithm to Explore the 3C Product Engineering Change
指導教授:林文燦林文燦引用關係黃喬次黃喬次引用關係
指導教授(外文):Wen-Tsann LinChiao-Tzu Huang
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:工業工程與管理系
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:2009
畢業學年度:97
語文別:中文
論文頁數:122
中文關鍵詞:六標準差工程變更IDEF基因演算法產品生命週期管理
外文關鍵詞:Six SigmaEngineering ChangeIDEFGenetic AlgorithmProduct Lifecycle Management
相關次數:
  • 被引用被引用:7
  • 點閱點閱:315
  • 評分評分:
  • 下載下載:37
  • 收藏至我的研究室書目清單書目收藏:0
在全球化競爭與2008年全球金融風暴的雙重壓力下,台灣科技產業面臨更加激烈的競爭,且在產品生命週期日益縮短的趨勢下,產品工程變更(EC)的快速回應(QR)便成為企業在此競爭環境下的關鍵成功因素。在產品生命週期中的工程變更,可能影響產品品質、成本與期程的因素很多,企業競爭模式也已經從傳統的單打獨鬥變成現今的協同合作(Collaboration)。因此本論文嘗試應用六標準差(6σ)的問題分析與解決步驟來進行探討,以個案公司在產品工程變更中所面臨專案之工作項目與作業部門的排程問題切入,首先利用整合性電腦輔助製造定義模式(IDEF)定義產品工程變更管理系統;透過資料蒐集計劃(DCP)衡量其作業流程之時間與部門間協同合作相關性;再以排程理論之甘特圖(Gantt Chart)分析相關作業處理時間;接著以基因演算法(GA)為基礎,改善現有工程變更流程中工作指派最大完工時間(Makespan),並建立一套最佳化求解程序;最後應用產品生命週期管理(PLM)系統控制工程變更工作流程之最佳化模式,並對個案公司應用PLM系統的實質效益作說明。

本論文以傳統之排程理論求得工程變更最大完工時間為179hrs.,後續經由基因演算法分析求解後,其最佳之最大完工時間為168hrs.。研究結果顯示,基因演算法的最大完工時間明顯的優於傳統排程理論之最大完工時間,且平均相對改善率為5.76%,故基因演算法為一個具有良好績效的演算法。透過基因演算法探討工程變更相關作業,不僅可以取得最大完工時間最小化,並可大量縮短個案公司在指派工程變更專案之排程時間;同時藉由本論文提出之研究分析模式,做為業界結合六標準差手法與基因演算法導入產品工程變更工作指派之參考依據。

Under the dual pressure of globalization competitiveness and 2008 worldwide financial crisis, Taiwan technology industry is confronting with stiffer competition. Under the trend of increasingly shortening of product lifecycle, quick response of product engineering change becomes the key successful factor in the competitive environment. There are many factors which may influence product quality, cost, and duration when engineering change takes place in product lifecycle. The competitiveness of enterprises from the traditional model of their own has become the current collaboration. Therefore, this paper attempts to apply problem solution mode of Six Sigma (6σ) and focuses in the scheduling problem of work project and operating department when product engineering change takes place in the case study company. First of all, Integrated Computer-aided Manufacturing Definition (IDEF) mode is used to define engineering change management system. With the use of Data Collection Plan (DCP), the time of operating procedure is measured and Inter-sectoral collaboration correlation. Next, Scheduling Theory integrated with the applicable rules is applied to analyze relevant operating procedures. Genetic Algorithm (GA) is then used as the basic in developing heuristic algorithm to improve the maximum makespan of the task dispatching in existing engineering change procedure. At the same time, a set of optimization solution procedure is established. Lastly, engineering change workflow module of Product Lifecycle Management (PLM) system is used to control optimization mode, and illustrates the substantial benefits of PLM system application in the case study company.

This paper utilizes traditional Scheduling Theory in obtaining engineering change maximum makespan of 179 hours. Following with the analysis and solution by GA, the best maximum makespan is 168 hours. Research findings show that the maximum makespan of GA apparently better than the traditional Scheduling Theory. Besides, the average error percentage of 5.76% shows that GA is an algorithm with good results. Examine engineering change relevant operations with GA not only obtaining minimize makespan but also shorten great deal of engineering change project assignment scheduling time in case study company. At the same time, the analysis mode brought up by this paper is to be made as reference for the industry in the implementation of 6σintegrated with GA in product engineering change task dispatching.

中文摘要 i
英文摘要 ii
誌謝 iv
目錄 vi
表目錄 ix
圖目錄 x
符號說明 xii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 研究對象及範圍 4
1.4 研究限制 5
1.5 研究流程 6
第二章 文獻探討 8
2.1 六標準差基本概論 8
2.1.1 六標準差的起源與發展 8
2.1.2 六標準差管理架構 11
2.1.3 六標準差成功關鍵因素 13
2.1.4 六標準差的推行現況 16
2.2 工程變更 18
2.2.1 工程變更之定義 18
2.2.2 工程變更發生之原因 19
2.2.3 工程變更流程說明與步驟 20
2.2.4 工程變更的應用 23
2.3 整合性電腦輔助製造定義模式 25
2.4 資料蒐集計劃 28
2.5 零工式生產排程系統 30
2.5.1 排程問題的特性 30
2.5.2 Job-Shop Scheduling問題的相關排程技術 31
2.6 基因演算法 32
2.6.1 基因演算法簡介 33
2.6.2 基因演算法之步驟 33
2.6.3 基因演算法之應用 35
2.7 產品資料管理與產品生命週期管理 37
2.7.1 產品資料管理之定義 37
2.7.2 產品資料管理系統功能與架構 38
2.7.3 產品生命週期管理 40
第三章 研究方法 42
3.1 IDEF0定義工程變更管理系統 43
3.2 資料蒐集計劃現況衡量 44
3.3 甘特圖分析工程變更完工時間 45
3.4 基因演算法建構最佳化求解程序 46
3.4.1 基因演算法之流程 47
3.4.2 基因演算法之建構 48
3.4.3 基因演算法之演算步驟 51
3.4.4 基因演算法演算架構 55
3.5 產品生命週期管理系統之應用 56
第四章 六標準差專案實證分析 58
4.1 定義階段 58
4.2 衡量階段 63
4.3 分析階段 67
4.4 改善階段 72
4.4.1 問題與符號定義 73
4.4.2 演算流程與步驟 78
4.4.3 實驗結果 79
4.5 控制階段 83
4.5.1 產品生命週期之系統架構 83
4.5.2 產品生命週期系統之應用 85
4.5.3 系統效益分析 93
4.6 結果分析與討論 95
第五章 結論與建議 97
5.1 結論 97
5.2 建議 98
參考文獻 100
中文部分 100
英文部分 102


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