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研究生:董至軒
研究生(外文):Chih-Hauan Tung
論文名稱:離散型細菌分群演算法求解多途程單元形成問題
論文名稱(外文):Automatic clustering for cell formation with alternative process routings using a Discrete Bacterial Foraging Optimization
指導教授:高有成高有成引用關係
指導教授(外文):Yucheng Kao
口試委員:高有成
口試委員(外文):Yucheng Kao
口試日期:2014-07-03
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:2014
畢業學年度:102
語文別:中文
論文頁數:114
中文關鍵詞:粒子群演算法細菌演算法單元形成問題替代途程
外文關鍵詞:bacterial foraging optimizationalternative routingscell formation problemparticle swarm optimization
相關次數:
  • 被引用被引用:1
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  • 下載下載:10
  • 收藏至我的研究室書目清單書目收藏:0
資料分群技術是單元製造系統設計最常見的解題方法之一,目的是將各種相似機器與零件歸類(分群規劃成製造單元)。過去研究大部分單元數必須事先決定,在實務上受限制,目前只有極少數研究者考慮單元數不須事先決定,在單元形成過程去搜尋最佳單元數稱為自動分群,類似資料分群事先不給定分群組數,是極為困難的問題。本研究提出離散型細菌最佳化演算法,結合粒子群演算法的自己最佳及全體最佳的方法,分別考量無順序及有順序加工下,解決自動分群多途程單元形成問題,並期望能產生最佳單元形成結果。實驗結果顯示,本研究所提出離散行細菌演算法能自動決定機器單元數,不論是否有考慮加工順序,都可以有效解決多途程單元形成問題。
The use of data clustering methods is one of the most popular approaches of designing cellular manufacturing systems (CMS). The aim of cell formation is to classify similar parts into part families and to group machines into manufacturing cells. In most past studies, the number of machine cells is known beforehand, but in practice there are many restrictions to obtain the number in advance. This study proposed an automatic clustering approach based on discrete bacterial foraging optimization (DBFO) algorithms to solve cell formation problems with alternative process routings. That is, the best number of machine cells is searched during the cell formation process, not given in advance. To enhance the searching ability of DBFO, the concept of gbest and pbest adopted from particle swarm optimization (PSO) is embedded into the proposed algorithm. Two types of generalized cell formation problems are tested: considering operation sequences and not considering operation sequences. The experimental results show that the proposed DBFO algorithm performs well for solving cell formation problems, particularly when the cell numbers are not given beforehand.
誌謝 I
摘要 II
Abstract III
圖次 VI
表次 VII
第壹章 簡介 1
第一節 研究背景與動機 1
第二節 研究範圍與限制 2
第三節 研究流程 3
第四節 論文架構 5
第貳章 文獻探討 6
第一節 單元形成定義 6
第二節 求解單元形成介紹 10
第三節 粒子群最佳演算法 13
第四節 細菌覓食最佳演算法 13
第參章 方法論 17
第一節 數學模型 17
第二節 DBFO演算法 22
第三節 解的表達 25
第四節 機器分群 26
第五節 零件與途程指派 28
第六節 單元數演化 30
第七節 不合理解的處理 30
第八節演化流程 32
第肆章 範例說明 36
第伍章 實驗與比較 44
第陸章 結論 54
參考文獻 56
附錄 62
圖次
圖1 - 1研究流程圖 4
圖2 - 1單途程初始矩陣 6
圖2 - 2單途程單元形成結果 6
圖2 - 3無加工順序性多途程初始矩陣 7
圖2 - 4無加工順序多途程單元形成結果 8
圖2 - 5有加工順序多途程初始矩陣 9
圖2 - 6有加工順序多途程單元形成結果 9
圖3 - 1零件觀點搬運次數計算 22
圖3 - 2機器觀點搬運次數計算 22
圖3 - 3 DBFO演算流程圖 24
圖3 - 4解的第二部分連續解對應真實單元的離散 25
圖3 - 5連續解與離散解轉換圖 25
圖3 - 6閥值α演化變動 28
圖3 - 7有加工順序單元形成問題零件選擇途程 29
圖3 - 8有加工順序單元形成問題零件分群 29
圖4 - 1為第一隻細菌解初始單元形成 37
圖4 - 2零件途程選擇結果 41
圖4 - 3單元形成結果 41
表次
表2 - 1多途程文獻整理表 12
表4 - 1為隨機產生六隻細菌初始解 36
表4 - 2第一隻細菌解初始翻滾方向 37
表4 - 3第一隻細菌解連續變數初始值 37
表4 - 4初始細菌解與Pbest和Gbest 38
表4 - 5更新細菌連續變數 38
表4 - 6機器的相似係數矩陣表47細菌離散指派新位置 38
表4 - 7第一隻細菌解指派機器至新單元之 40
表4 - 8細菌目標函式排序 42
表4 - 9複製操作結果表411複製操作結 43
表4 - 10驅散操作結果 43
表5 - 1 無加工順序文獻比較結果 46
表5 - 2有加工順序單元形成文獻比較結果 49
表5 - 3第5題參數調整的10題解比較結果 50
表5 - 4無加工順序DBFO改良前後實驗結果 52
表5 - 5有加工順序DBFO改良前後實驗結果 53
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