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研究生:林芳君
研究生(外文):Fang-Jun Lin
論文名稱:應用粒子群最佳化於群集分析以縮短SMT換線時間-以研華科技為例
論文名稱(外文):Application of Particle Swarm Optimization to Clustering Analysis for Reducing SMT Setup Time- A Case Study on Advantech Company
指導教授:郭人介郭人介引用關係
口試委員:駱至中吳建文田方治
口試日期:2007-06-12
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:工業工程與管理研究所
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:2007
畢業學年度:95
語文別:中文
論文頁數:87
中文關鍵詞:粒子群最佳化群集分析表面黏著技術
外文關鍵詞:Particle Swarm OptimizationClustering AnalysisSMT (Surface Mount Technology)
相關次數:
  • 被引用被引用:6
  • 點閱點閱:325
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  • 下載下載:1
  • 收藏至我的研究室書目清單書目收藏:0
由於工業電腦產業為少量多樣的生產型態,使得SMT(Surface Mount Technology)生產系統在換線作業中面臨重大挑戰,時常未能在下一張訂單開始之前完成人工備料及綁料作業,導致昂貴機台閒置及產能利用率降低。因此本研究利用粒子群最佳化發展出一套BPSO(Binary Particle Swarm Optimization)clustering群集分析方法,其結合區域搜尋方法K-means,以加快找到全域最佳解,且加入輪盤選擇的操作,減少陷入區域最佳解的現象。
本研究分別先採用4個已知群集分佈的基準資料集Iris、Wine、Vowel及Glass作為比較依據,驗證不同群集方法分群的準確性,再將其應用在研華科技新店單板廠機種BOM(Bill of Materials)表資料,藉由產品族的概念,找出相同族群的機種共用料,將同一族群之機種安排在一起生產,便能縮短SMT換線作業時間,以因應市場少量多樣的生產模式。
Due to characteristics of high mix low volume for industrial personal computer (IPC), SMT (Surface Mount Technology) production system faces a great challenge that the engineer can not complete the material preparation process before next order coming. It results in idling expensive machine and decreasing capacity utilization. Therefore, this research intends to employ particle swarm optimization (PSO) with integrating K-means method and roulette selection operator in order to accelerate searching speed and avoid falling into local solution.
For the purpose of evaluation, we first use four benchmark datasets- Iris, Wine, Vowel, and Glass as the comparison basis in order to verify the accuracy for each clustering method. The simulated results have shown the proposed method is able to cluster the data more precisely than the convention methods, like PSO and K-means. Moreover, the proposed method is applied to cluster the BOM (Bill of Materials) for Advantech Company which is a very famous IPC manufacturer. According to the concept of product family, the clustering results can be used to find the shared materials for each product family. Thus, the engineer can arrange the orders belonging to the same family to be manufactured together in order to reduce the SMT setup time. The results have shown this capability for the proposed method.
目錄

摘要 i
Abstract ii
誌謝 iv
目錄 v
表目錄 viii
圖目錄 x
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍 2
1.4 研究架構 3
第二章 文獻探討 5
2.1 工業電腦產業 5
2.1.1 工業電腦定義與特性 5
2.1.2 工業電腦與一般電腦的差異 6
2.1.3 工業電腦產品種類 9
2.2 表面黏著技術 10
2.3 群集分析 13
2.3.1 群集分析之程序 13
2.3.2 群集分析之方法 15
2.4 粒子群最佳化 22
2.4.1 粒子群最佳化之發展背景 23
2.4.2 粒子群最佳化演算法 23
2.4.3 二進制粒子群最佳化 28
2.4.4 粒子群最佳化和基因演算法之比較 29
2.4.5 粒子群最佳化之相關應用 30
第三章 研究方法 34
3.1 研究流程 34
3.2 BPSO clustering 35
3.2.1 BPSO clustering編碼方式 36
3.2.2 BPSO clustering演算流程 37
3.3 效能驗證 41
第四章 實驗分析 42
4.1 資料集介紹 42
4.2 資料前處理 44
4.3 演算法評估準則 44
4.4 實驗結果與分析 45
4.4.1 演算法結果分析 45
4.4.2 演算法收斂情形 47
4.5 PSO參數分析 49
第五章 實證分析 53
5.1 實證分析流程 54
5.2 實證資料來源及前處理 54
5.3 實證結果與分析 56
5.3.1 ART2神經網路群數的決定 57
5.3.2 分群效果比較 58
5.3.3 實例分群結果 59
5.3.4 BPN於訂單之分類 61
5.4 換線效益評估 63
5.5 結語 70
第六章 結論與建議 71
6.1 研究結論 71
6.1.1 實驗分析結論 71
6.1.2 實證分析結論 71
6.2 研究貢獻 72
6.3 未來研究與建議 72
參考文獻 73
附錄A 實驗結果 80
附錄B 主成份分析結果 84
附錄C 實證結果 87










表目錄

表1-1 機種特徵表 3
表2-1 工業電腦和消費性電腦的差異 8
表4-1 資料集整理 42
表4-2 Iris Data Set 43
表4-3 Glass Data Set 43
表4-4 演算法分群結果 46
表4-5 學習因子 及 之參數分析 50
表4-6 慣性權重 之參數分析 51
表4-7 粒子數之參數分析 52
表5-1 各因素主成份分析 56
表5-2 不同分群群數之Wilk’s Lambda值(1) 58
表5-3 不同分群群數之Wilk’s Lambda值(2) 58
表5-4 各群集分析方法SED值 59
表5-5 各群集機種數目及共用料數目 59
表5-6 不同的網路架構之比較 61
表5-7 不同的學習率及慣性因子之比較 62
表5-8 SMT兩日生產計畫產品之隸屬群集 62
表5-9 SMT兩日生產計畫 63
表5-10 改善前-FCFS生產排程之生產資訊 64
表5-11 改善前-SPT生產排程之生產資訊 66
表5-12 改善後-FCFS生產排程之生產資訊 67
表5-13 改善後-SPT生產排程之生產資訊 69
表5-14 改善前及改善後換線效益結果 70

























圖目錄

圖1-1 研究架構圖 4
圖2-1 傳統電子元件製程示意圖 11
圖2-2 表面黏著電子元件示意圖 11
圖2-3 SMT生產流程圖 12
圖2-4 群集分析之程序 14
圖2-5 凝聚法與分裂法之階層式分群法 17
圖2-6 PSO演算法示意圖 24
圖2-7 粒子速度與位置搜尋示意圖 25
圖3-1 群集分析方法評估流程 35
圖3-2 BPSO clustering之編碼 36
圖3-3 BPSO clustering之particle轉換流程 37
圖3-4 BPSO clustering演算流程 40
圖4-1 各演算法於Iris Data Set收斂圖 47
圖4-2 各演算法於Wine Data Set收斂圖 48
圖4-3 各演算法於Vowel Data Set收斂圖 48
圖4-4 各演算法於Glass Data Set收斂圖 48
圖5-1 實證分析流程圖 55
圖5-2 機種與用料報表圖 56
圖5-3 改善前-FCFS生產排程甘特圖 65
圖5-4 改善前-SPT生產排程甘特圖 66
圖5-5 改善後-FCFS生產排程甘特圖 68
圖5-6 改善後-SPT生產排程甘特圖 69
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