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Evolutionary Computing in Advanced Manufacturing.
"This cutting-edge book covers emerging, evolutionary and nature inspired optimization techniques in the field of advanced manufacturing. The complexity of real life advanced manufacturing problems often cannot be solved by traditional engineering or computational methods. Hence, in recent year...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Somerset :
Wiley,
2011.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Half Title page; Title page; Copyright page; Preface; List of Contributors; Chapter 1: Production Planning Using Genetic Algorithm; 1.1 Introduction; 1.2 Production Planning Models; 1.3 Genetic Algorithm; 1.4 Implementation of GA; 1.5 Summary; Further Reading; Chapter 2: Process Planning through Ant Colony Optimization; 2.1 Introduction; 2.2 Ant Colony Optimization (ACO); References; Chapter 3: Introducing a Hybrid Genetic Algorithm for Integration of Set Up and Process Planning; 3.1 Introduction; 3.2 Process Planning; 3.3 Machine Set-up Time; 3.4 Chromosome Representation.
- 3.5 Fitness Value Evaluation3.6 Selection Operation; 3.7 Crossover Operations; 3.8 Mutation Operations (k-opt Exchange); 3.9 Conclusion; References; Chapter 4: Design for Supply Chain with Product Development Issues Using Cellular Particle Swarm Optimization (CPSO) Technique; 4.1 Introduction; 4.2 Problem Formulation; 4.3 Computational Analysis and Result; 4.4 Conclusions; References; Chapter 5: Genetic Algorithms with Chromosome Differentiation (GACD) Based Approach for Process Plan Selection Problems; 5.1 Introduction; 5.2 Problem Formulation.
- 5.3 Genetic Algorithm with Chromosome Differentiation5.4 GACD Based Solution Methodology to Process Plan Selection Problem; 5.5 Numerical Experiments; 5.6 Conclusions; References; Chapter 6: Operation Allocation in Flexible Manufacturing System Using Immune Algorithm; 6.1 Introduction; 6.2 Machine Loading Problem; 6.3 Solution Methodology; 6.4 Implementing Immune Algorithm for Machine Loading Problem; 6.5 Computational Result; 6.6 Conclusion; References; Chapter 7: Tool Selection in FMS A Hybrid SA-Tabu Algorithm Based Approach; 7.1 Introduction; 7.2 Literature Survey; 7.3 Problem Formulation.
- 7.4 Background on SA-Tabu Heuristic7.5 Implementation of Tabu-Simulated Annealing; 7.6 Test Cases; 7.7 Conclusion; References; Chapter 8: Integrating AGVs and Production Planning with Memetic Particle Swarm Optimization; 8.1 Introduction; 8.2 Literature Review; 8.3 Mathematical Model; 8.4 PSO and EMPSO; 8.5 Example; 8.6 Recombination (Local Search); 8.7 Summary; References; Chapter 9: Simulation-Based Aircraft Assembly Planning Using a Self-Guided Ant Colony Algorithm; 9.1 Introduction; 9.2 Background and Literature Survey; 9.3 Specifications of the Considered Aircraft Assembly.
- 9.4 Proposed Simulation-Based Assembly Planning Framework9.5 Experiment and Results; 9.6 Conclusion and Future Work; References; Chapter 10: Applications of Evolutionary Computing to Additive Manufacturing; 10.1 Introduction; 10.2 Design for Additive Manufacturing; 10.3 Data Handling; 10.4 Process Planning; 10.5 Concluding Remarks; References; Chapter 11: Multiple Fault Diagnosis Using Psycho-Clonal Algorithms; 11.1 Introduction; 11.2 Multiple Fault Diagnosis Problems; 11.3 Background of Psychoclonal Algorithm; 11.4 Numerical Experiments; 11.5 Conclusion; References.