16. General structure of genetic algorithms

21. 輪盤法 (Roulette wheel) 其中 f i 為染色體 i 的適應性值；如此適應度越高則所佔的面積就越大。假設對此輪盤射一飛鏢，當然面積越大的就越容易射中，如此被複製的機率就越高，重複此步驟，直到複製完相等於母體數的染色體。

22. 研究重點 2 客製化為基的規劃模式 (Customization manufacturing) 1 以 connector 為基的組裝規劃模式 5 基因演算法 (Genetic algorithms ) 6 群蟻演算法 (Ant colony algorithms) 7 模擬退火法 (Simulated annealing algorithms) 3 模組化議題的探討 (Product modularity) 4 綠色環保議題的探討 (Design for environment)

23. 以 connector 為基的 組裝規劃模式 1 Journal of Intelligent Manufacturing , 10, 423-435 (1999)

25. Connector 觀念及其相關工程資訊 Connector 是以零件間的「結合型態」作為產品的描述依據，本身扮演著設計階段觀念層次的建構單元，故可包含著更多工程資訊。 運用啟發式求解組裝順序 。

26. Connector 為基與 GAs 結合 2 International Journal of Production Research , 42(11), 2243-2261 (2004)

28. 本研究重心

30. Classification of fastener types 3 Races and ball-bearing balls MND Not disassembled 1 snap ring, bearing, spring MD Disassembled Movable fastener 4 pressing fits, riveted joints, welding FND Not disassembled 2 Screw, bolted joint, key, spline, wedge FD Disassembled Fixed fastener level Example Code Type

31. Example: stapler (a) Part drawing (a) Part information Pivot rod 18 Guide rod 9 Rivet4 17 Bottom track 8 Rivet3 16 Staple spring 7 Fastener piece 15 Slide foot 6 Rivet bottom 14 Pivot spring 5 spring 13 Steel top 4 base 12 Rivet1 3 Impact plate 11 Bracket spring 2 Rivet2 10 Steel cover 1 Part name Part No. Part name Part No.

32. connector-based precedence graph.

33. Note: (1) FD: Fixed fastener disassembled (2) FND: Fixed fastener Not disassembled (3) MD:Movable fastener disassembled (4) MND: Movable fastener Not disassembled (5) T 1 : hand (6) T 2 : screwdriver (7) T 3 : a hand vice Connector information of stapler 1,4,8,12,18 T 3 z FND Interference fit C 8 1,2,3 T 3 y FND Interference fit C 7 6,5,4 T 1 -y MD Snap fit C 6 8,9 T 1 x FND Insert C 5 6,7 T 1 -x MD Spring C 4 6,9 T 1 -x FND Insert C 3 7,9 T 1 -x MD Spring C 2 12,15,16,17 T 3 y FND Interference fit C 1 10,11,12,13,14 T 3 -y FND Interference fit C 0 Component owned by connector Tool Direction Combination type Connector name No.

34. Connector object (Mapping from 2(b))

35. Mapping to C ++ code

36. Class init_genetic

38. Fitness function

40. Crossover operators(PMX)

41. Crossover operators(PMX)

42. Insert mutation method

43. ga class

45. Calculate the fitness value Connector information Generate initial populations Reproduction: Roulette Wheel method PMX crossover Insert mutation method Optimal solutions obtained (1) (2) (3) (4) (5) (6) (7) No Satisfy the stopping criteria ? Generate new population number of populations, mutation rate, crossover rate, stopping criteria. (1) Yes The flow chart that combines the connector concept and the GAs

46. Case study – a computer Part drawing

47. Computer: connector-based precedence graph

48. Interface of the computer program with the GAs

50. Convergence plot of stapler Convergence plot of computer hard disk

52. Guided-GAs 引導式基 因演算法 3 International Journal of Production Research, 44(3), 601-625 (2006)

53. 引導式基因演算法流程圖

56. 引導式基因演算法

57. 引導式基因演算法

59. 引導式基因演算法

62. 引導式基因演算法 Step4 ：依據公式 (4) 計算染色體位置 i 之 Connector 與 i+1 位置之 Connector 工程相似度，佔染色體位置 i 之 Connector 與其 它 Connector 中相似度最大之比例。

63. 引導式基因演算法 Step5 ：判斷 p 與 Ri 大小。 (a). 若 Ri 大於等於 p ，則 i 與 Block-size 加一，並且重 複 Step3 至 Step5 步驟。 (b). 若 Ri 小於 p ，且 Block-size 小於三分之一的染色 體長度，則對 i 加一並且重複 Step2 至 Step5 步驟。 否則進行 Step6 步驟。 Step6 ：終止搜尋並且以 Block-start 為保留區間之起始 位置，而 Block-size 為保留區間之大小。

65. 引導式基因演算法

68. 範例測試

69. 電風扇交配率為 70% 、突變率為 30% 、母體大小為 51 、最大世代數為 1500 代 的測試環境 0 137 17.3333 16.6666 Guided-GAs 6 879 17 6.5999 Traditional-GAs Times of infeasible solution Average generations of convergence Max fitness value Average fitness value Method

70. Convergence plot of electrical fan.

71. Part drawing of the laser printer 0 76.6666 75.5333 Guided-GAs 10 0 0 Traditional-GAs Times of infeasible solution Max fitness value Average fitness value Method

74. Memetic Algorithms 改 良式基因演算法 4 Expert Systems with Applications 33(2), 451-467 (2007)

75. 改良式基因演算法 否 是 是 否

80. 範例測試 (2/11) 5.67 5.627 MAs 5.67 5.495 Guided-GAs 最大適應值 平均適應值 方法

82. 範例測試 (4/11)

84. 範例測試 (6/11)

85. 範例測試 (8/11) 81 79.596 90.89 MAs 77.66 76.862 118.691 Guided-GAs 最大適應值 平均適應值 平均時間 方法

87. Artificial Immune Systems for Exploring Assembly Sequence Planning 5 Engineering Applications of Artificial Intelligence 22(8), 1218-1232 (SCI) (2009)

94. Stapler: connector-based precedence graph

95. Successor Lists, SL

96. Predecessor Lists, PL

97. 為組裝規劃而設計類免疫演算法流程圖

98. OX 交配操作程序圖

99. Stapler: 單點突變操作程序

100. 最佳抗體 次佳抗體 1 次佳抗體 2 相同個數 k = 6 66.7 % 相同個數 k = 5 55.6 % 100% 親和力挑選示意圖

101. Comparison between three algorithms for fan. 18.667 18.365 3.045 AIAs 18.333 18.285 3.951 Memetic Algorithms 16.667 16.133 2.808 Guided-GAs Max objective value Average objective value Average time Method

102. Convergence plot of electric fan

103. Comparison between three algorithms for laser printer 82.33 81.432 19.067 AIAs 80 79.096 25.965 Memetic Algorithms 76.67 75.595 18.543 Guided-GAs Max objective value Average objective value Average time Method

106. 綠色導向產品模組化之研究 Modular design to support green-life cycle engineering 7 Expert Systems with Application, 34, 2524-2537 (2008)

111. Proposed Framework

112. Liaison graph of a pen (a) (b) A higher LI indicates a more difficult type of combination and a smaller LI means a simpler type of combination. Liaison intensity(LI)

114. Table 1 Intensity of contact type Strong combination high Score Many faces will be contacted. 30 Multi-face contact Many points will be contacted. 24 Multi-point contact The contact part is a face. 18 Single face contact The contact part is a line . 12 Line contact The contact part is a point. 6 Point contact Description Liaison intensity Attribute Contact type

116. Encoding for grouping genetic algorithms (GGA). Each gene stands for a module. For a chromosome composed of five modules “ABCDE”, the number of modules can be expressed as A={1}, B={3, 6}, C={4}, D={2}, E={5}.

117. Fitness Design A stronger Li intra indicates that it is easy to assemble components in a module

118. Crossover for GGA

119. Crossover for GGA Reinsert part 6

122. Case study- table lamp. 22 components and 22 liaisons

123. Table 5 Estimate liaison intensity for table lamp Create the Liaison Intensity for every components. 40 1 Angle Hand Turn on PC 13-14 40 1 Angle Hand Turn on PC 12-14 60 1 Angle Small tool type Put on PC 11-14 36 1 Angle Hand Insert PC 10-14 45 4 Angles Hand Insert MPC 9-10 30 5 Angles Hand Insert LC 8-10 26 5 Angles Hand Put on LC 7-10 54 1 Angle Small tool type Turn on PC 6-20 54 1 Angle Small tool type Turn on PC 6-19 54 1 Angle Small tool type Turn on PC 5-18 54 1 Angle Small tool type Turn on PC 5-17 54 1 Angle Small tool type Turn on PC 5-16 54 1 Angle Small tool type Turn on PC 5-15 32 5 Angles Hand Put on SFC 5-10 60 1 Angle Hand Insert MFC 3-6 54 1 Angle Small tool type Turn on PC 2-22 54 1 Angle Small tool type Turn on PC 2-21 30 5 Angles Hand Insert LC 2-8 26 5 Angles Hand Put on LC 2-7 32 5 Angles Hand Put on SFC 1-6 48 1 Angle Hand Insert SFC 1-4 32 5 Angles Hand Put on SFC 1-2 Liaison intensity Accessed direction Tool type Combination type Contact type Liaison

124. Interface for liaison intensity estimation. Boland C++6.0

125. The results obtained from the GGA. Five modules can be get.

126. Eco-indicator99 Situation 1 Component 8 Situation 2 Whole module 3,6,19,20 0.16 0.3 240 Cast iron 0.00125 Screw8 22 0.16 0.3 240 Cast iron 0.00125 Screw7 21 0.16 0.3 240 Cast iron 0.00125 Screw6 20 0.16 0.3 240 Cast iron 0.00125 Screw5 19 0.16 0.3 240 Cast iron 0.00125 Screw4 18 0.16 0.3 240 Cast iron 0.00125 Screw3 17 0.16 0.3 240 Cast iron 0.00125 Screw2 16 0.16 0.3 240 Cast iron 0.00125 Screw1 15 9 16.5 330 Plastic 0.05 A_plug 14 1.8 3.3 330 Plastic 0.01 Fuse2 13 1.8 3.3 330 Plastic 0.01 Fuse1 12 14.07 25.8 86 Steel 0.30 Transformer 11 7.2 13.2 330 Plastic 0.04 Base 10 1.8 3.3 330 Plastic 0.01 Power 9 *26.17 48 240 Cast iron 0.20 Soft_pipe 8 1.8 3.3 330 Plastic 0.01 Plastic 7 9 16.5 330 Plastic 0.05 Contact 6 4.69 8.6 86 Steel 0.1 Steel2 5 0.47 0.86 86 Steel 0.01 Steel1 4 1.11 2.04 51 Glass 0.04 Bulb 3 5.4 9.9 330 Plastic 0.03 Cover2 2 14.39 26.4 330 Plastic 0.08 Cover1 1 % Poll Indicator Material Weight Name Component

128. Table 7 Material cost and process cost change of Component 8. Change the material to reduce the green polluted value. Choose the alternative material whose pollution and cost are lower. The material and process costs will be changed when the material is changed. 5.1 1.0 5.1 4.76 1.0 4.76 C pc T pcu C pcu C pc T pcu C pcu Cost Weight Unit Cost Weight Unit Process cost 0.6 0.2 3.0 0.56 0.2 2.8 C m W m C mu C m W m C mu Cost Weight Unit Cost Weight Unit Material cost After modification Before modification

129. Fig. 7. (a) Illustration for light bulb module, (b) a revised modular graph for the table lamp. (a) (b) Situation 2 A new design replace the original component 3, 6 ,19 and 20.

132. 應用多目標混合基因演算法 整合組裝規劃與線平衡之研究 8 International Journal of Production Research, 21(1), 5951-5977 (2008)

134. 研究動機與背景 (2/6) ASP ALB ASP ALB 顧客導向 CE Consider Simultaneously Local optimal

135. 研究動機與背景 (3/6) ASP 規劃 ALB 規劃 組裝在先關係圖 彈性組裝系統 WS01 WS02 WS03 WS04 A B C D Flow 1 2 3 4 5 7 6 8 3 5 6 3 5 6

137. 研究動機與背景 (5/6) 多目標混合基因演算法 (Multi-objective hybrid genetic algorithms, MOHGA) 進化式多目標最佳化 ( Evolutionary multi-objective optimization, EMO ) 基因多目標規劃 Efficiency Performance 彈性組裝系統 集群基因演算法 (Grouping genetic algorithms, GGA) 序列式基因演算法 ( G enetic algorithms, GAs) 多目標規劃

139. 研究方法 關聯圖為基的產品模型 ASP 與 ALB 規劃分析 MOHGA 架構 決策分析 兼顧 ASP 與 ALB 的組裝線設計

140. ASP 規劃分析 Liaison graph Assembly precedence graph EX:1 -> 2 -> 4 -> 3 -> 6 -> 5 -> 7 -> 8 1 2 3 4 5 7 6 8 p1 p2 p3 p4 p5 p6

142. ASP 與 ALB 小結 {{1, 2}, {3, 4, 5, 6},{7, 8}} 方向性 : ± X 、 ± Y 、 ± Z 工具種類 : T 1 、 T 2 、 T 3 、 T 4 1 2 3 4 5 7 6 8 Liaison 編號 組裝時間 組裝工具 組裝方向 1 11 T 1 -y 2 17 T 1 x 3 9 T 3 x 4 5 T 1 -x 5 8 T 3 -y 6 12 T 3 z 7 10 T 2 z 8 3 T 4 x 4 -> 3 -> 6 -> 5 3 -> 4 -> 6 -> 5 3 -> 5 -> 4 -> 6 4 -> 3 -> 5 -> 6 3 -> 4 -> 5 -> 6 Liaisons 組裝順序

148. 多目標混合基因演算法 編碼 產生初始解 局部搜尋 計算目標函數 選擇 交配 突變 局部搜尋 精華保留策略 更新柏拉圖最佳解 是否終止 結束 開始

151. 參數設定

157. 結果分析 (1/4) 柏拉圖最佳解 散佈圖 ( Kilbridge ) 柏拉圖最佳解 超平面 ( Kilbridge ) 柏拉圖最佳解 超平面 ( Warnecke ) 柏拉圖最佳解 超平面 ( Wee-Mag )

158. 結果分析 (2/4) 不均衡狀態函數值 收斂圖 ( Kilbridge ) 工具變換耗時函數值 收斂圖 ( Kilbridge ) 方向變換耗時函數值 收斂圖 ( Kilbridge ) 柏拉圖最佳解個數 趨勢圖 ( Kilbridge )

159. 結果分析 (3/4) 目標一與目標二的 散佈圖 ( Kilbridge ) 目標一與目標三的 散佈圖 ( Kilbridge ) 目標二與目標三的 散佈圖 ( Kilbridge ) 正規化公式

160. 結果分析 (4/4) 目標函數之盒型圖 ( Kilbridge )

161. 柏拉圖最佳解決策分析 (1/2)

165. 編碼表示法 序列式染色體 群組導向染色體

166. 解碼程序 Back 1 2 3 4 5 7 6 8

177. Mass customization 9 Expert Systems with Applications 29, 913-925 (2005)

183. CBR 系統

187. Blue pen vs. green pen BOM Blue Green

192. 產品結構之 CBR 演算法流程

194. 2 Enclosure guarding 2 Turret size 3 Eliminates truncating device 9 Turret type 1 Oil bump Turret 1 Cooling system 5 Main spindle horsepower 1 Lubrication pump 7 Main spindle thimble Standard accessories 10 Main spindle Rotational speed 4 Z-axis servo motor 10 Main spindle tip 3 Z-axis transmission type 10 Spindle bore 2 Z-axis feeding rate Headstock 6 Z-axis speedy feeding 15 Bed length 1 X-axis servo motor 1 Rail type 2 X-axis 3 Carriage width 1 X-axis feeding rate 4 CNC controller 2 X-axis speedy feeding 15 Center distance Feeding 2 Carriage width 1 Tailstock quill 2 Cross slide length 2 Tailstock quill movement 6 Center height 1 Tailstock body movement 6 Rail radius 1 Tailstock thimble 6 Bed radius 1 Tailstock quill diameter 9 Model two Tailstock 3 Model one Option Feature Option Feature

195. 產品特徵輸入介面

196. 分析 & 評估產品特徵樹