Evolutionary Computing in Advanced Manufacturing

Evolutionary Computing in Advanced Manufacturing......Page 5
Contents......Page 7
Preface......Page 15
List of Contributors......Page 19
1.1 Introduction......Page 25
1.2 Production Planning Models......Page 26
1.2.1 Mathematical Model......Page 27
1.3 Genetic Algorithm......Page 33
1.3.1 Procedure of Genetic Algorithm (GA)......Page 34
1.4.1 Algorithm Parameters: (Population Size, Probability of Crossover and Mutation)......Page 39
1.4.2 Parameter Tuning......Page 40
Further Reading......Page 42
2.1 Introduction......Page 43
2.2 Ant Colony Optimization (ACO)......Page 49
2.2.1 Problem Description......Page 51
2.2.2 Case Problem......Page 52
2.2.3 Results......Page 55
References......Page 57
3. Introducing a Hybrid Genetic Algorithm for Integration of Set Up and Process Planning......Page 61
3.2 Process Planning......Page 62
3.3 Machine Set-up Time......Page 63
3.3.1 Optimization Methodology: Genetic Algorithms (GA)......Page 65
3.4 Chromosome Representation......Page 67
3.5 Fitness Value Evaluation......Page 68
3.6 Selection Operation......Page 69
3.8 Mutation Operations (k-opt exchange)......Page 71
References......Page 72
4. Design for Supply Chain with Product Development Issues Using Cellular Particle Swarm Optimization (CPSO) Technique......Page 75
4.1 Introduction......Page 76
4.2 Problem Formulation......Page 79
4.2.1 Notations......Page 80
4.2.2 Simulated Problem......Page 84
4.2.3 Particle Swarm Algorithm (PSO)......Page 87
4.2.4 Cellular Particle Swarm Optimization (CPSO) Algorithm......Page 91
4.2.5 CPSO-outer Algorithm......Page 93
4.3 Computational Analysis and Result......Page 95
4.4 Conclusions......Page 98
References......Page 99
5.1 Introduction......Page 101
5.2 Problem Formulation......Page 104
5.3.1 Overview of GA......Page 105
5.3.3 Description of GA with Chromosome Differentiation......Page 106
5.4 GACD Based Solution Methodology to Process Plan Selection Problem......Page 110
5.5 Numerical Experiments......Page 114
References......Page 116
6. Operation Allocation in Flexible Manufacturing System Using Immune Algorithm......Page 119
6.1 Introduction......Page 120
6.2 Machine Loading Problem......Page 124
6.2.1 Problem Formulation......Page 127
6.3.1 Introduction to Immune System and Analogy to Immune Algorithm......Page 130
6.3.2 Modified Immune Algorithm Used to Solve Machine Loading Problem (Prakash et al. 2008)......Page 132
6.4 Implementing Immune Algorithm for Machine Loading Problem......Page 137
6.5 Computational Result......Page 138
6.6 Conclusion......Page 141
References......Page 143
7. Tool Selection in FMS A Hybrid SA-Tabu Algorithm Based Approach......Page 147
7.1 Introduction......Page 148
7.2 Literature Survey......Page 149
7.3 Problem Formulation......Page 151
7.4.1 Simulated Annealing......Page 154
7.4.2 Tabu Search......Page 155
7.5.1 Notations Used in SA-Tabu Heuristic......Page 157
7.5.2 Steps of the Hybrid SA-Tabu Heuristic......Page 158
7.5.3 Representation......Page 159
7.5.4 Search Parameters......Page 160
7.6 Test Cases......Page 163
7.7 Conclusion......Page 168
References......Page 172
8.1 Introduction......Page 175
8.1.1 Production and AGVs Scheduling......Page 177
8.2 Literature Review......Page 178
8.3.2 Mathematical Programming Model......Page 179
8.4 PSO and EMPSO......Page 183
8.5 Example......Page 185
8.6 Recombination (Local Search)......Page 187
References......Page 190
9. Simulation-Based Aircraft Assembly Planning Using a Self-Guided Ant Colony Algorithm......Page 193
9.1 Introduction......Page 194
9.2.1 Assembly Planning in Aircraft Manufacturing......Page 196
9.2.2 Self-Guided Ant Colony Algorithm......Page 200
9.3 Specifications of the Considered Aircraft Assembly......Page 201
9.4.1 Overview of the Proposed Framework......Page 203
9.4.2 Mathematical Formulation......Page 207
9.4.3 Details of Self Guided Ant Colony Algorithm (SGAC)......Page 208
9.5 Experiment and Results......Page 213
9.5.1 Effect of Rework on the Total Lead Time......Page 215
9.6 Conclusion and Future Work......Page 216
References......Page 217
10. Applications of Evolutionary Computing to Additive Manufacturing......Page 221
10.1 Introduction......Page 222
10.2.1 Structural Design......Page 224
10.2.2 Functional Grading......Page 227
10.2.3 Digital Design/Art......Page 229
10.2.4 Inspired by Nature......Page 232
10.2.5 Future Challenges......Page 234
10.3 Data Handling......Page 236
10.4.1 Build Packing......Page 240
10.4.2 Part Orientation......Page 247
10.4.3 Slicing......Page 250
10.4.4 Parameter Optimisation......Page 253
10.4.5 Summary......Page 255
References......Page 256
11. Multiple Fault Diagnosis Using Psycho-Clonal Algorithms......Page 259
11.1 Introduction......Page 260
11.2 Multiple Fault Diagnosis Problems......Page 261
11.3.1 Artificial Immune System (AIS)......Page 266
11.3.2 Theory of Clonal Selection......Page 268
11.3.3 Maslow's Need Hierarchy Theory......Page 270
11.3.4 Pseudo Code for Psycho Clonal Algorithm......Page 272
11.4.1 Test Problems......Page 274
11.4.2 Results and Discussions......Page 276
11.5 Conclusion......Page 278
References......Page 281
12.1 Introduction......Page 283
12.2 Background......Page 285
12.3 Problem Description......Page 287
12.3.1 Problem Statement......Page 288
12.3.2 Formulation of the Model......Page 289
12.4 Evolutionary Solution Approaches......Page 292
12.4.2 Genetic Algorithm with Integer Programming (GAIP)......Page 293
12.4.3 Pure Probability Based Heuristic Approach......Page 295
12.5.1 Example......Page 296
12.5.3 Results and Analysis Using GAIP......Page 297
12.5.4 The Solution Quality of PHA and Comparison with the GAIP Approach......Page 299
12.5.5 Results When Demand of Each Product is Represented as a Probability Distribution......Page 304
12.6. Conclusion and Recommendations for Future Research......Page 307
References......Page 309
13.1 Introduction......Page 313
13.2 Literature Review......Page 316
13.3.1 Fundamental Variables Discussion......Page 318
13.3.2 Partner Selection Problem Description......Page 319
13.4.1 Particle Swarm Optimization......Page 321
13.4.2 Ant Colony Optimization......Page 323
13.4.3 Hybrid PSO-ACO......Page 324
13.4.4 Weights of the Criteria and the Qualitative Variables......Page 327
13.5 Experimental Analysis......Page 332
13.5.1 Determine the Weights of the Main Criteria and Sub-Criteria......Page 333
13.5.2 Evaluation of Qualitative Attributes......Page 337
13.5.4 Results......Page 340
13.6 Conclusion......Page 343
Nomenclature......Page 344
References......Page 348
Index......Page 351