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研究生:劉威信
研究生(外文):Wei-Shin Liu
論文名稱:應用蜂群最佳化於設施佈置問題之研究
論文名稱(外文):Applying Marriage in Honey-Bees Optimization on the Facilities Layout Problem
指導教授:林志平林志平引用關係王明展王明展引用關係
指導教授(外文):Chih-Ping LinMing-Jaan Wang
口試委員:楊明峰
口試委員(外文):Ming-Feng Yang
口試日期:2012-06-20
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:工業工程與管理系碩士班
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:2012
畢業學年度:100
語文別:中文
論文頁數:70
中文關鍵詞:蜂群最佳化設施佈置問題
外文關鍵詞:Marriage in Honey-Bees OptimizationFacility Layout Problem
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設施佈置問題(Facility Layout Problem, FLP)是企業相當重視且投入眾多資源的一項問題,設施佈置是依據部門之間人力、物料流量與設備的相互影響,來使設施與位置的對應關係達到最佳化的狀態,使空間得以充分被利用,故一個規劃良好且有效率的設施佈置對未來作業的成本與效率影響極大。
在過去研究中,學者們陸續發展各種啟發式演算法來求解設施佈置問題。蜂群最佳化(Marriage in Honey-Bees Optimization, MBO)是近年來新興發展的演算法,常被用來求解不同領域問題,其求解品質亦不遜於其他演算法。因此本研究將使用蜂群最佳化來應用在設施佈置問題上,希望能有更好的求解效率及品質。本研究針對間斷型設施佈置問題來做探討,以蜂群最佳化為基礎應用,透過其搜尋與交配特性,來求解設施佈置問題,並與過去其他演算法比較,期能找到更好的佈置方案,讓企業於營運時能有更低的成本及更短的物料搬運距離。


Facility layout problem (FLP) is the problem that the enterprises put more attention on. Facility layout problem depends on the human resources, the material flow and the equipment to get the optimal status of the layout and the maximization of space utilization rate. The facility layout that has well planning and great efficiency can generate the monumental effect on the operation of future.
In past research, scholars invent many heuristic algorithms to solve facility layout problems. Marriage in Honey-Bees Optimization (MBO) is the algorithm that is purposed recently. MBO is usually used to get solution in many fields, and its solution quality is not worse than other algorithms. Furthermore, MBO is never used in facility layout problems. So, this study will use MBO to get a better solution and a shorter solution time. This study focuses on the discrete layout problem, and applies MBO to solve the kind of problem. The research will present the comparison with algorithms. We expect to find a better layout plan that can efficiently reduce cost and material handling distance.


目錄
摘 要 i
ABSTRACT ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍與假設 3
1.4 研究流程 4
第二章 文獻探討 6
2.1 設施佈置 6
2.1.1 系統化圖形技巧 6
2.1.2 最佳解數學規劃與啟發式演算法 7
2.2 萬用啟發式演算法(Meta-heuristic algorithms) 9
2.2.1 模擬退火法應用於間斷型設施佈置 10
2.2.2 基因演算法應用於間斷型設施佈置 12
2.2.3 小結 15
2.3 蜂群最佳化 15
2.3.1 蜂群最佳化之理念 15
2.3.2 蜂群最佳化區域搜尋法則 16
第三章 模式建立與求解方法 20
3.1 建立目標模式 20
3.2 蜂群最佳化之步驟 21
3.3 案例說明 27
第四章 實驗設計與案例結果分析 40
4.1 測試案例說明 40
4.2 參數設計 41
4.2.1 決定控制因子(Factor)與水準(Level)數 42
4.2.2 田口直交表 43
4.2.3 田口實驗 44
4.3 例題驗證 46
4.3.1 間斷型部門面積相等案例 47
4.3.2 間斷型部門面積不相等案例 52
第五章 結論與未來研究方向 63
5.1 結論 63
5.2 研究貢獻 64
5.3 未來研究方向與建議 64
參考文獻 65

表目錄
表2.1 萬用啟發式演算法分類 10
表2.2 Ku et al.(2010)基因編碼 11
表2.3 Islier(1998)基因編碼 13
表2.4 Wang et al.(2005)基因編碼 14
表2.5 蜂群最佳化應用領域之文獻整理 19
表3.1 各部門所需面積 27
表3.2 流量從至圖 29
表3.3 初始蜂群 30
表3.4 各部門中心位置座標 32
表3.5 蜂后精巢I 34
表3.6 交配後產生的子代I 34
表3.7 改善後的子代1(子代1’) 35
表3.8 改善後的子代排序I 36
表3.9 新的蜂群 36
表3.10 蜂后精巢II 38
表3.11 交配後產生的子代II 38
表3.12 改善後的子代排序II 39
表4.1 程式執行環境 40
表4.2 測試案例試題 41
表4.3 控制因子與水準數 42
表4.4 L9(34)直交表 43
表4.5 L9(34)直交表轉換參數 43
表4.6 參數組合與S/N值 44
表4.7 因子水準之S/N回應值表 45
表4.8 各因子最佳參數組合 46
表4.9 部門面積相等案例執行結果 47
表4.10 部門面積相等測試結果與文獻比較分析 50
表4.11 部門面積不相等案例執行結果 52
表4.12 部門面積不相等案例1測試結果與文獻比較分析 55
表4.13 部門面積不相等案例2測試結果與文獻比較分析 56
表4.14 部門面積不相等案例3測試結果與文獻比較分析 57
表4.15 部門面積不相等案例4測試結果與文獻比較分析 58
表4.16 部門面積不相等案例5測試結果與文獻比較分析 59
表4.17 部門面積不相等案例6測試結果與文獻比較分析 60

圖目錄
圖1.1 間斷型設施佈置 2
圖1.2 連續型設施佈置 2
圖1.3 研究流程圖 5
圖2.1 空間填滿曲線佈置 11
圖2.2 基因編碼設施佈置 12
圖2.3 三種常用Hilbert曲線 13
圖2.4 四種常用部門掃瞄(擺放)策略 13
圖2.5 基因編碼設施佈置 14
圖2.6 蜂群最佳化理念 16
圖2.7 蜂群最佳化架構圖 17
圖3.1 蜂群最佳化運作流程 22
圖3.2 蜂群最佳化應用於設施佈置之求解程序 26
圖3.3 廠房形狀示意圖 28
圖3.4 廠房sweeping band設定示意圖 28
圖3.5 正方形廠房之佈置順序 29
圖3.6 蜂后初始佈置 31
圖3.7 雄蜂1初始佈置 31
圖3.8 雄蜂2初始佈置 31
圖3.9 改善後的子代1(子代1’)設施佈置 35
圖4.1 各因子水準S/N值回應圖 46
圖4.2 部門面積相等案例最佳MFFC收斂情形 49
圖4.3 部門面積相等最佳佈置圖解 51
圖4.4 部門面積不相等案例最佳MFFC收斂情形 54
圖4.5 部門面積不相等案例1最佳佈置圖解 60
圖4.6 部門面積不相等案例2最佳佈置圖解 61
圖4.7 部門面積不相等案例3最佳佈置圖解 61
圖4.8 部門面積不相等案例4最佳佈置圖解 61
圖4.9 部門面積不相等案例5最佳佈置圖解 62
圖4.10 部門面積不相等案例6最佳佈置圖解 62









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