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研究生:張謙閔
研究生(外文):Chien-Min Chang
論文名稱:兩階段等效平行機台於流程式生產系統批量流之研究
論文名稱(外文):A Study of Lot-Streaming in Two-Stage Flow-Shop with Parallel Machine
指導教授:柯千禾柯千禾引用關係駱景堯駱景堯引用關係
學位類別:碩士
校院名稱:大葉大學
系所名稱:工業工程與科技管理學系
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:2007
畢業學年度:95
語文別:中文
論文頁數:84
中文關鍵詞:兩階段流程式生產系統批量流粒子最佳化演算法混合型基因演算法
外文關鍵詞:Two-stage flow-shop systemLot streamingParticle swarm optimization (PSO)Hybrid genetic algorithm (HGA)
相關次數:
  • 被引用被引用:1
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  • 收藏至我的研究室書目清單書目收藏:1
批量流的概念已被廣泛的應用在實務的生產中,並且能得到不錯的成效,然而批量流的相關研究上仍有發展的空間;因此,本研究將批量流的技術加入具平行機台的生產系統中,並且進行深入的研究與探討。
本研究中探討流程式生產批量流技術,在多產品兩階段的生產環境中,加入了整備時間、搬運時間和移運設備容量等考量,以總完工時間最小化為目標下,首先以數學規劃法建構此問題的模式以求得問題的最佳解,但隨著問題複雜度提高,最佳解的求得變的過於耗時,故本研究以粒子最佳化演算法(PSO)為基礎,發展一可求解間斷型問題的啟發式演算法,以快速尋求一近似最佳解,並與混合型基因演算法(HGA)進行比較,探討兩種啟發式演算法之求解能力。
本研究運用參數分析求得兩種啟發式演算法最適參數組合,分別測試其求解績效並分析差異,研究結果顯示混合型基因演算法具有較佳的求解能力;另外,在批量分割對總完工時間的實驗中,驗證批量分割所帶來的改善效益隨子批分割數增加而增加,但改善的幅度有逐漸遞減的趨勢。
Lot streaming has been widely implemented in a number of production system and shown favorable results; but still there is space of development.
In this research, we study the multi-job lot streaming problem which setup time and transportation time is considered in two stage flowshop with parallel machine. The objective is to minimize the makespan. First, a 0-1 integer programming model is constructed; however, the mathematical model is too much time consuming to solve the medium or large size problem. We propose two heuristic algorithms to get a near optimal schedule in a reasonable computation time. One is that particle swarm optimization based heuristic another one is combined with genetic algorithm and particle swarm optimization that two kinds are performed in heuristics.
During the research, the parameters used in the heuristics that affect the solution quality and efficiency are designed and analyzed; then for the constructed heuristics, a good parameter setting is suggested. The experimental results are reported and provided for the references for the further research.
目錄

封面內頁
簽名頁
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
圖目錄 x
表目錄 xi
第一章 緒論 1
1.1 研究動機與背景 1
1.2 研究目的 2
1.3 研究範圍與限制 3
1.4 研究方法 4
1.5 研究架構 5
第二章 文獻探討 6
2.1 多階段流程式生產系統排程研究 6
2.2 批量流於排程問題 8
2.2.1 時間模式 10
2.2.2 成本模式 12
2.3 粒子群最佳化演算法 13
2.4 基因演算法 15
第三章 數學規劃求解模式之建構 20
3.1 符號說明 20
3.2 批量流模式之建構 22
3.3 範例說明 27
第四章 啟發式演算法之建構 29
4.1 粒子群最佳化演算法之建立 29
4.1.1 粒子群最佳化演算法建構流程 30
4.1.2 候選名單之建立 32
4.2 基因演算法之建立 37
4.2.1 基因演算法建構流程 37
4.2.2 簡單遺傳基因演算法介紹 39
4.2.3 應用PSO於子批大小求解 43
4.3 起始解 44
4.3.1 NEH法之運用 44
4.3.2 強森法則之運用 45
第五章 實驗結果分析 46
5.1 實驗數據與參數設定 46
5.2 粒子群最佳化演算法之參數分析 48
5.3 混合型基因演算法之參數分析 54
5.4 數學模式與啟發式演算法之結果比較 59
5.5 PSO與HGA之分析比較 61
5.6 批量分割對總完工時間的影響 62
第六章 結論與建議 65
6.1 結論 65
6.2 未來研究建議 66
參考文獻 67
附錄一 啟發式演算法之參數分析 72
附錄二 批量分割之變異數分析表 83
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