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研究生:傅士誠
研究生(外文):Shih-cheng Fu
論文名稱:以螞蟻分群演算法設計製造單元
論文名稱(外文):An Ant-based Clustering Algorithm for Manufacturing Cell Design
指導教授:高有成高有成引用關係
學位類別:碩士
校院名稱:大同大學
系所名稱:資訊經營學系(所)
學門:商業及管理學門
學類:一般商業學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:72
中文關鍵詞:群組技術單元形成螞蟻演算法集群分析
外文關鍵詞:Ant AlgorithmCell FormationCluster AnalysisGroup Technology
相關次數:
  • 被引用被引用:2
  • 點閱點閱:171
  • 評分評分:
  • 下載下載:21
  • 收藏至我的研究室書目清單書目收藏:1
單元製造為群組技術最主要的應用,需要一個有效的零件分群法則,來進行初步製造單元設計。集群分析法為常用方法之一,搭配特殊設計的相似係數,能集群相似度高的零件成為零件家族。集群分析法可分為階層分群與非階層分群兩種,但階層分群法容易有鏈結效應,而非階層分群事先需給定零件家族數目。故在本研究中提出一個以人工螞蟻分群模式為基之零件分群演算法,來解決上述問題。本演算法利用螞蟻分群特性:群體性與隨機性,使零件分群過程有再指派的機制,降低例外資料對分群結果之影響。另外,本演算法利用螞蟻的自我組織能力,自然地形成數群相似度甚高的零件家族,並利用嵌入合併法則的螞蟻去達到規劃者理想群數。本演算法已經被開發成APCS(Ant-based Part Clustering System)系統,並利用分群績效指標,對十五個文獻案例進行測試,都得到相當好的單元形成結果。最後與七種傳統集群分析法進行比較,顯現出本演算法在零件分群方面確實有較佳表現。
Cellular Manufacturing is one of the major applications of group technology. It requires an effective part clustering approach to execute preliminary manufacturing cell design. One of famous approaches is the cluster analysis method, which uses similarity coefficients and clustering methods to group similarity parts into part families. Clustering methods are divided into two categories: hierarchical and nonhierarchical methods. Hierarchical methods often suffer from chaining effects, while nonhierarchical methods need a predetermined cluster number. The research proposes a part clustering algorithm that is based on an artificial ant clustering model. The algorithm utilizes the characteristics of ants, congregation and randomness, to prevent grouping results from being fixed during clustering processes and to reduce the effects of noisy data. Besides, the algorithm has the ability of self-organization to form high homogeneous part families naturally. The algorithm has been developed to an ant-based part clustering software system (APCS). Fifteen literature problems were selected to test the proposed algorithm with respect to group efficancy. We found that the algorithm is able to obtain better machine cell configurations. A comparative study was also conducted to compare the algorithm with seven conventional clustering methods, and the results showed that the algorithm appears to outperform most of conventional methods.
摘要 I
Abstract II
致謝 III
Table of Contents IV
List of Figures VI
List of Tables VII
Chapter1 Introduction 1
1.1 The Background and Motivation of Research 1
1.2 The Objective of Research 2
1.3 The Flow Chart of Research 5
1.4 The Scope and Limitation of Research 8
1.5 The Framework of thesis 9
Chapter2 Literature Review 10
2.1 Group Technology 10
2.2 Cell Formation Methods 11
2.3 Cluster Analysis Method 15
2.3.1 Similarity Coefficient 15
2.3.2 Hierarchical Clustering Method 17
2.3.3 Non-Hierarchical Clustering Method 19
2.3.4 Two-Phase Cluster Analysis 20
Chapter3 Ant Clustering Model 22
3.1 Swarm Intelligence 22
3.2 The Introduction of Ant Clustering Model 22
3.3 The Application of Ant Clustering Model 25
Chapter4 Ant Clustering Algorithm 28
4.1 An Ant-based Part Clustering Algorithm 28
4.1.1 Initializing Phase 29
4.1.2 Producing Part Families 29
4.1.3 Refining Part Families 34
4.1.4 Combining Part Families 35
4.2 The Flow of Algorithm 37
4.3 Ant-based Part Clustering System(APCS) 38
4.3.1 Part Family Formation 39
4.3.2 Machine Assignment 39
4.3.3 Performance Evaluation 39
Chapter5 Case Verification and Comparison 42
5.1 Case Verification 42
5.1.1 Part Family Formation 42
5.1.2 Machine Assignment 47
5.1.3 Performance Evaluation 47
5.2 Comparative Study 48
5.2.1 Performance Consistency Measurement 50
5.2.2 Best Known Solution Comparative 52
5.2.3 Comparison with Cluster Analysis Method 53
5.3 Brief Summary 57
Chapter6 Conclusion and Future Work 58
6.1 Conclusion 58
6.2 Future Work 59
Bibliography 61
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