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研究生:吳鈴淳
研究生(外文):Ling-Chun Wu
論文名稱:以兩階段基因免疫演算法改良生存策略求解流程型排程問題
論文名稱(外文):Two-phase Genetic-Immune Algorithm with Improved SurvivalStrategy of Lifespan for Flow-shop Scheduling Problems
指導教授:張百棧張百棧引用關係
學位類別:碩士
校院名稱:元智大學
系所名稱:工業工程與管理學系
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:2009
畢業學年度:97
語文別:中文
論文頁數:92
中文關鍵詞:基因演算法類免疫演算法排程問題共演化策略生存策略
外文關鍵詞:Genetic AlgorithmArtificial Immune SystemScheduling ProblemsCo-evolutional processSurvival Strategy
相關次數:
  • 被引用被引用:5
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雖然基因演算法(Genetic Algorithm, GA)為一著名的求解組合性問題的工具,但面臨過早收斂而陷入了局部最佳化的缺點。類免疫演算法(Artificial Immune System, AIS)與基因演算法一樣具有多點同步搜尋的特性,不同於基因演算法的是,其使用了記憶訓練方式與不同抗體對抗入侵的抗原變化,除了針對抗體與抗原的匹配性,也考慮抗體與抗體之間的關係,能彌補過去基因演算法的系統機制只能針對染色體單純特徵組合求解的缺陷,有鑑於此,本研究混合基因加免疫的演算法(HGIA),輔以共演化策略改善,並將生命力賦予染色體,作為生存策略,期望在搜尋空間尋找更多樣化的解,另外,為兼顧收斂速度與探索空間,本篇加入了株落選擇機制,以人造複製的T細胞及突變的B細胞共同演化出辨識率更高之抗體,透過此機制之優良探究與探索特性,可求得近似最佳解。本研究求解單目標流程型(Flowshop)排程問題,其績效準則為以總完工時間最小化為目標,因GA之快速收歛特性,故在第一階段以GA進行演化,並於第二階段以HGIA進一步搜尋更寬廣之解空間。研究顯示,本研究所提之TPGIA,在特定問題上可提升原有SGA之求解品質。
In this paper, a Hybrid Genetic-Immune algorithm (HGIA) is developed to solve the flow-shop scheduling problems. The regular genetic algorithm (GA) is applied in the first-stage to rapidly evolve and when the processes are converged up to a pre-defined iteration then the Artificial Immune System (AIS) is introduced to hybridize Genetic Algorithm in the second stage is named HGIA. In the process of co-evolution, GA and AIS cooperates with each other to search optimal solution by searching different objective functions. One is named fitness in GA section and another one is antigen which will evoke the withstanding of antibodies. In the process of fighting, the antibodies evolve till they can resist the antigen. Moreover, an survival strategy is proposed to extend the lifespan of the antibodies to stay in system longer. Finally, Clonal selection is adopted into the infrastructure of AIS which contains certain types of B and T lymphocytes are selected for destruction of specific antigens invading the body. Due to the hybrid of GA and AIS contains two objectives, larger searching space and escaping from local optimal solution will be the superiority for hybridization. In the research, a set of flow-shop scheduling problems are applied for validating the efficiency. The intensive experimental results show the effectiveness of the proposed approach for Flow-shop problems in Production Scheduling.
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 2
1.3 研究方法 3
1.4 研究架構與流程 4
第二章 文獻探討 7
2.1 排程問題 7
2.1.1 排程類型 7
2.1.2 排程的績效目標 9
2.1.3 排程問題的求解方法 11
2.1.4 排程問題之相關研究 13
2.2 基因演算法 15
2.2.1 基因演算法的流程 15
2.2.2 基因演算法的特性及優缺點 17
2.2.3 基因演算法之相關研究 19
2.3 類免疫演算法 21
2.3.1 類免疫演算法的流程 22
2.3.2 類免疫演算法的特性及優缺點 24
2.3.3 類免疫演算法之相關研究 26
2.4 基因演算法與類免疫演算法之異同點 27
2.5 共演化之相關研究 28
第三章 問題定義與介紹 30
3.1 流程型排程介紹 30
3.2 流程型排程之績效衡量準則 31
3.3 流程型排程測試案例 32
3.3.1 Taillard流程型排程測試資料 32
3.3.2 Reeves流程型排程測試資料 33
3.4 流程型排程問題之範圍限制與假設 33
第四章 研究方法 35
4.1 免疫系統親和力計算方式 38
4.2 TPGIA演算流程 40
4.3 欲比較之演算法介紹 49
第五章 實驗結果與分析 51
5.1 於Taillard測試案例之流程型排程問題 51
5.1.1 Taillard測試案例之參數設計 51
5.1.2 求解於Taillard測試案例之實驗結果與效益評估 52
5.2 於Reeves測試案例之流程型排程問題 57
5.2.1 Reeves測試案例之參數設計 57
5.2.2 求解於Reeves測試案例之實驗結果與效益評估 58
5.3 綜合討論與分析 62
第六章 結論與未來展望 64
6.1 結論 64
6.2 未來展望 66
參考文獻 68
附錄A. Taillard測試案例之實驗設計 73
A.1 SGA於Taillard測試案例之參數設計與實驗結果 73
A.1.1 SGA於Taillard測試案例(同Ruiz等人[37]執行次數)之實驗設計 73
A.1.2 SGA於Taillard測試案例(同Lian等人[27]執行次數)之實驗設計 75
A.2 TPGIA於Taillard測試案例之參數設計與實驗結果 77
A.2.1 TPGIA於Taillard測試案例(同Ruiz等人[37]執行次數)之實驗設計 78
A.2.2 TPGIA於Taillard測試案例(同Lian等人[27]執行次數)之實驗設計 80
附錄B. Reeves測試案例之實驗設計 83
B.1 SGA於Reeves測試案例之參數設計與實驗結果 83
B.2 TPGIA於Reeves測試案例之參數設計與實驗結果 85
附錄C. 於Taillard測試案例在流程型排程問題上之實驗結果 89
C.1 各方法於Taillard測試案例(同Ruiz等人[37]執行次數)之實驗結果 89

表目錄
表2-1 生產排程類型說明 8
表2-2 排程績效目標 10
表2-3 排程績效目標分類 11
表2-4 求解排程問題法則 12
表2-5 排程之相關研究 14
表2-6 基因演算法與類免疫演算法之比較 27
表3-1 工件加工時間表 32
表4-1 四個工件於兩部機台之加工時間 39
表4-2 抗體基因資訊之優勢矩陣 45
表4-3 位置2之每個工件的機率及累加機率 46
表4-4 基因位置之投票結果 49
表4-5 位置3之每個工件的機率及累加機率 50
表5-1 於Taillard測試案例之建議參數 52
表5-2 於Taillard測試案例之最大搜尋次數 52
表5-3 於Taillard測試案例(同Ruiz等人[37]執行次數)之實驗結果 53
表5-4 於Taillard測試案例(同Ruiz等人[37]執行次數)之平均誤差率(%) 54
表5-5 於Taillard測試案例(同Ruiz等人[37]執行次數)ANOVA分析 54
表5-6 於Taillard測試案例(同Lian等人[27]執行次數)之實驗結果 56
表5-7 於Taillard測試案例(同Lian等人[27]執行次數)之平均誤差率(%) 56
表5-8 於Taillard測試案例(同Lian等人[27]執行次數)ANOVA分析 57
表5-9 於Reeves測試案例之建議參數 57
表5-10 於Reeves測試案例之最大搜尋次數 57
表5-11 於Reeves測試案例之實驗結果 58
表5-12 於Reeves測試案例之平均誤差率(%) 59
表5-13 於Reeves測試案例ANOVA分析 59
表6-1 各方法之特色及優缺點 65
表A-1 SGA於Taillard測試案例之因子設計 73
表A-2 SGA於Taillard測試案例(同Ruiz等人[37]執行次數)ANOVA分析 74
表A-3 SGA於Taillard測試案例(同Ruiz等人[37]執行次數)之參數設定 75
表A-4 SGA於Taillard測試案例(同Lian等人[27]執行次數)ANOVA分析 76
表A-5 SGA於Taillard測試案例(同Lian等人[27]執行次數)之參數設定 77
表A-6 TPGIA於Taillard測試案例(同Ruiz等人[37]執行次數)之因子設計 78
表A-7 TPGIA於Taillard測試案例(同Ruiz等人[37]執行次數)ANOVA分析 78
表A-8 TPGIA於Taillard測試案例(同Ruiz等人[37]執行次數)之參數設定 80
表A-9 TPGIA於Taillard測試案例(同Lian等人[27]執行次數)之因子設計 80
表A-10 TPGIA於Taillard測試案例(同Lian等人[27]執行次數)ANOVA分析 81
表A-11 TPGIA於Taillard測試案例(同Lian等人[27]執行次數)之參數設定 82
表B-1 SGA於Reeves測試案例之因子設計 83
表B-2 SGA於Reeves測試案例ANOVA分析 83
表B-3 SGA於Reeves測試案例之參數設定 85
表B-4 TPGIA於Reeves測試案例之因子設計 86
表B-5 TPGIA於Reeves測試案例ANOVA分析 86
表B-6 TPGIA於Reeves測試案例之參數設定 88

圖目錄
圖1-1 研究架構圖 6
圖2-1 基因演算法基本流程圖 16
圖2-2 類免疫演算法示意圖 22
圖2-3 抗原與抗體結合示意圖 23
圖3-1 排列式流程型排程示意圖 31
圖3-2 排列式流程示意圖 32
圖4-1 TPGIA演算流程圖 36
圖4-2 抗原( )與抗體( )間親和力計算解說圖 38
圖4-3 親和力計算 39
圖4-4 順序編碼模式 40
圖4-5 順序編碼之雙點交配 42
圖4-6 順序編碼之移動式突變 42
圖4-7 參與投票的六條抗體資訊 45
圖4-8 株落選擇示意圖 47
圖4-9 AC2GA演算流程圖 50
圖5-1 SGA、AC2GA及TPGIA在案例ta021之收斂圖 55
圖5-2 SGA、AC2GA及TPGIA在案例ta081之收斂圖 55
圖5-3 SGA、AC2GA及TPGIA在案例rec27之收斂圖 60
圖5-4 (a)~(f) SGA、AC2GA及TPGIA於Reeves各問題上之收斂圖 61
圖A-1 SGA於Taillard測試案例(同Ruiz等人[37]執行次數)之主效應圖 74
圖A-2 SGA於Taillard測試案例(同Ruiz等人[37]執行次數)之交互作用圖 75
圖A-3 SGA於Taillard測試案例(同Lian等人[27]執行次數)之主效應圖 76
圖A-4 SGA於Taillard測試案例(同Lian等人[27]執行次數)之交互作用圖 77
圖A-5 TPGIA於Taillard測試案例(同Ruiz等人[37]執行次數)之主效應圖 79
圖A-6 TPGIA於Taillard測試案例(同Ruiz等人[37]執行次數)之交互作用圖 79
圖A-7 TPGIA於Taillard測試案例(同Lian等人[27]執行次數)之主效應圖 81
圖A-8 TPGIA於Taillard測試案例(同Lian等人[27]執行次數)之交互作用圖 82
圖B-1 SGA於Reeves測試案例之主效應圖 84
圖B-2 SGA於Reeves測試案例之交互作用圖 85
圖B-3 TPGIA於Reeves測試案例之主效應圖 87
圖B-4 TPGIA於Reeves測試案例之交互作用圖 87
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