Evolutionary Computing in Advanced Manufacturing
Published on 26. August 2011
Software
Other digital
354 pages
978-1-118-16188-3 (ISBN)
Description
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 years researchers and practitioners have proposed and developed new strands of advanced, intelligent techniques and methodologies. 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. In the later chapters, modified and improved versions of evolutionary algorithms are discussed. The book concludes with appendices which provide general descriptions of several evolutionary algorithms.
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Dimensions
Height: 250 mm
Width: 150 mm
Thickness: 15 mm
Weight
666 gr
ISBN-13
978-1-118-16188-3 (9781118161883)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Manoj Tiwari | Jenny A. Harding
Evolutionary Computing in Advanced Manufacturing
E-Book
07/2011
Wiley-Scrivener
€171.99
Available for download
Manoj Tiwari | Jenny A. Harding
Evolutionary Computing in Advanced Manufacturing
E-Book
07/2011
Wiley-Scrivener
€179.99
Available for download
Person
Manoj Tiwari is based at the Indian Institute of Technology, Kharagpur. He is an acknowledged research leader and has worked in the areas of evolutionary computing, applications, modeling and simulation of manufacturing system, supply chain management, planning and scheduling of automated manufacturing system for about 20 years. Jenny A. Harding joined Loughborough University in 1992 after working in industry for many years. Her industrial experience includes textile production and engineering, and immediately prior to joining Loughborough University, she spent 7 years working in R&D at Rank Taylor Hobson Ltd., manufacturers of metrology instruments. Her experience is mostly in the areas of mathematics and computing for manufacturing.
Content
Preface. 1. Production Planning using Genetic Algorithm (S. K. Kumar and M. K. Tiwari). 2. Process Planning through Ant Colony Optimization (Puneet Bhardwaj and M K. Tiwari). 3. Introducing a Hybrid Genetic Algorithm for Integration of Set Up and Process Planning (S. H. Chung and F. T. S. Chan). 4. Design for Supply Chain with Product Development Issues Using Cellular Particle Swarm Optimization (CPSO) Technique (Vikas Kumar and F. T. S. Chan). 5. Genetic Algorithms with Chromosome Differentiation (GACD) Based Approach for Process Plan Selection Problems (Nishikant Mishra and Vikas Kumar). 6. Operation Allocation in Flexible Manufacturing System Using Immune Algorithm (Mayank K. Pandey). 7. Tool Selection in FMS an Hybrid SA-TABU Algorithm Based Approach (Nitesh Khilwani, J. A. Harding and Nishikant Mishra). 8. Integrating AGVs and Production Planning with Memetic Particle Swarm Optimization (Sri Krishna, M. K. Tiwari and J. Harding). 9. Simulation-based Aircraft Assembly Planning Using a Self-Guided Ant Colony Algorithm (Sai Srinivas Nageshwaraniyer, Nurcin Celik, Young-Jun Son and Roberto Lu). 10. Applications of Evolutionary Computing to Additive Manufacturing (Candice Majewski). 11. Multiple Fault Diagnosis Using Psycho-Clonal Algorithms (Nagesh Shukla and Prakash). 12. Platform Formation Under Stochastic Demand (D. Ben-Arieh and A. M. Choubey). 13. A Hybrid Particle Swarm and Ant Colony Optimizer for Multi-attribute Partnership Selection in Virtual Enterprises (S. H. Niu, S. K. Ong and A. Y. C. Nee). Index.