Evolutionary Computing in Advanced Manufacturing

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ISBN: 9780470639245 | 0470639245
Cover: Hardcover
Copyright: 7/5/2011

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This cutting-edge book covers emerging, evolutionary and nature inspired optimization techniques in the field of advanced manufacturing. Evolutionary computing approaches are introduced in the context of a wide range of manufacturing activities, and through the examination of practical problems and their solutions, readers will gain confidence to apply these powerful computing solutions. The initial chapters introduce and discuss the well established evolutionary algorithm, to help readers to understand the basic building blocks and steps required to successfully implement their own solutions to real life advanced manufacturing problems.

Preface	p. xiii
List of Contributors	p. xvii
Production Planning Using Genetic Algorithm	p. 1
Introduction	p. 1
Production Planning Models	p. 2
Mathematical Model	p. 3
Genetic Algorithm	p. 9
Procedure of Genetic Algorithm (GA)	p. 10
Implementation of GA	p. 15
Parameter Tuning	p. 16
Summary	p. 18
Further Reading	p. 18
Process Planning through Ant Colony Optimization	p. 19
Introduction	p. 19
Ant Colony Optimization (ACO)	p. 25
Problem Description	p. 27
Case Problem	p. 28
Results	p. 31
References	p. 33
Introducing a Hybrid Genetic Algorithm for Integration of Set Up and Process Planning	p. 37
Introduction	p. 38
Process Planning	p. 38
Machine Set-up Time	p. 39
Optimization Methodology: Genetic Algorithms (GA)	p. 41
Chromosome Representation	p. 43
Fitness Value Evaluation	p. 44
Selection Operation	p. 45
Crossover Operations	p. 47
Mutation Operations (k-opt exchange)	p. 47
Conclusion	p. 48
References	p. 48
Design for Supply Chain with Product Development Issues Using Cellular Particle Swarm Optimization (CPSO) Technique	p. 51
Introduction	p. 52
Problem Formulation	p. 55
Notations	p. 56
Simulated Problem	p. 60
Particle Swarm Algorithm (PSO)	p. 63
Cellular Particle Swarm Optimization (CPSO) Algorithm	p. 67
CPSO-outer Algorithm	p. 69
Computational Analysis and Result	p. 71
Conclusions	p. 74
References	p. 75
Genetic Algorithms with Chromosome Differentiation (GACD) Based Approach for Process Plan Selection Problems	p. 77
Introduction	p. 77
Problem Formulation	p. 80
Genetic Algorithm with Chromosome Differentiation	p. 81
Overview of GA	p. 81
Genetic Algorithm Incorporating Chromosome Differentiation	p. 82
Description of GA with Chromosome Differentiation	p. 82
GACD Based Solution Methodology to Process Plan Selection Problem	p. 86
Selection of GACD's Parameter	p. 90
Numerical Experiments	p. 90
Conclusions	p. 92
References	p. 92
Operation Allocation in Flexible Manufacturing System Using Immune Algorithm	p. 95
Introduction	p. 96
Machine Loading Problem	p. 100
Problem Formulation	p. 103
Solution Methodology	p. 106
Introduction to Immune System and Analogy to Immune Algorithm	p. 106
Modified Immune Algorithm Used to Solve Machine Loading Problem (Prakash et al. 2008)	p. 108
Fast Clonal Algorithm (Khilwani et al., 2008)	p. 113
Implementing Immune Algorithm for Machine Loading Problem	p. 113
Computational Result	p. 114
Conclusion	p. 117
References	p. 119
Tool Selection in FMS A Hybrid SA-Tabu Algorithm Based Approach	p. 123
Introduction	p. 124
Literature Survey	p. 125
Problem Formulation	p. 127
Background on SA-Tabu Heuristic	p. 130
Simulated Annealing	p. 130
Tabu Search	p. 131
Simulated Annealing-Tabu	p. 133
Implementation of Tabu-Simulated Annealing	p. 133
Notations Used in SA-Tabu Heuristic	p. 133
Steps of the Hybrid SA-Tabu Heuristic	p. 134
Representation	p. 135
Search Parameters	p. 136
Test Cases	p. 139
Conclusion	p. 144
References	p. 148
Integrating AGVs and Production Planning with Memetic Particle Swarm Optimization	p. 151
Introduction	p. 151
Production and AGVs Scheduling	p. 153
AGVs Routing	p. 154
Literature Review	p. 154
Mathematical Model	p. 155
Problem Statement	p. 155
Mathematical Programming Model	p. 155
PSO and EMPSO	p. 159
Example	p. 161
Recombination (Local Search)	p. 163
Summary	p. 166
References	p. 166
Simulation-Based Aircraft Assembly Planning Using a Self-Guided Ant Colony Algorithm	p. 169
Introduction	p. 170
Background and Literature Survey	p. 172
Assembly Planning in Aircraft Manufacturing	p. 172
Self-Guided Ant Colony Algorithm	p. 176
Specifications of the Considered Aircraft Assembly	p. 177
Proposed Simulation-Based Assembly Planning Framework	p. 179
Overview of the Proposed Framework	p. 179
Mathematical Formulation	p. 183
Details of Self Guided Ant Colony Algorithm (SGAC)	p. 184
Experiment and Results	p. 189
Effect of Rework on the Total Lead Time	p. 191
Effect of Size of the Order on the Average Utilization of Workstations	p. 192
Conclusion and Future Work	p. 192
References	p. 193
Applications of Evolutionary Computing to Additive Manufacturing	p. 197
Introduction	p. 198
Design for Additive Manufacturing	p. 200
Structural-Design	p. 200
Functional Grading	p. 203
Digital Design/Art	p. 205
Inspired by Nature	p. 208
Future Challenges	p. 210
Data Handling	p. 212
Process Planning	p. 216
Build Packing	p. 216
Part Orientation	p. 223
Slicing	p. 226
Parameter Optimisation	p. 229
Summary	p. 231
Concluding Remarks	p. 232
References	p. 232
Multiple Fault Diagnosis Using Psycho-Clonal Algorithms	p. 235
Introduction	p. 235
Multiple Fault Diagnosis Problems	p. 237
Background of Psychoclonal Algorithm	p. 242
Artificial Immune System (AIS)	p. 242
Theory of Clonal Selection	p. 244
Maslow's Need Hierarchy Theory	p. 246
Pseudo Code for Psycho Clonal Algorithm	p. 248
Numerical Experiments	p. 250
Test Problems	p. 250
Results and Discussions	p. 252
Conclusion	p. 254
References	p. 257
Platform Formation Under Stochastic Demand	p. 259
Introduction	p. 259
Background	p. 261
Problem Description	p. 263
Problem Statement	p. 264
Formulation of the Model	p. 265
Evolutionary Solution Approaches	p. 268
Solution Encoding	p. 269
Genetic Algorithm with Integer Programming (GAIP)	p. 269
Pure Probability Based Heuristic Approach	p. 271
Extension to Independent Demand for Each Product	p. 272
Example Problem - Results and Discussions	p. 272
Example	p. 272
Results and Discussions	p. 273
Results and Analysis Using GAIP	p. 273
The Solution Quality of PHA and Comparison with the GAIP Approach	p. 275
Results When Demand of Each Product is Represented as a Probability Distribution	p. 280
Conclusion and Recommendations for Future Research	p. 283
References	p. 285
A Hybrid Particle Swarm and Ant Colony Optimizer for Multi-attribute Partnership Selection in Virtual Enterprises	p. 289
Introduction	p. 289
Literature Review	p. 292
Partner Selection Problem Formation	p. 294
Fundamental Variables Discussion	p. 294
Partner Selection Problem Description	p. 295
Solution Methodology	p. 297
Particle Swarm Optimization	p. 297
Ant Colony Optimization	p. 299
Hybrid PSO-ACO	p. 300
Weights of the Criteria and the Qualitative Variables	p. 303
Experimental Analysis	p. 308
Determine the Weights of the Main Criteria and Sub-Criteria	p. 309
Evaluation of Qualitative Attributes	p. 313
Evaluation of the Quantitative Aspects of the Enterprise	p. 316
Results	p. 316
Conclusion	p. 319
Nomenclature	p. 320
References	p. 324
Index	p. 327
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Evolutionary Computing in Advanced Manufacturing

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