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E-raamat: Evolutionary Computing in Advanced Manufacturing [Wiley Online]

,
  • Formaat: 354 pages
  • Sari: Wiley-Scrivener
  • Ilmumisaeg: 19-Jul-2011
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1118161882
  • ISBN-13: 9781118161883
Teised raamatud teemal:
  • Wiley Online
  • Hind: 206,17 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Evolutionary Computing in Advanced ManufacturingSuurem pilt
  • Formaat: 354 pages
  • Sari: Wiley-Scrivener
  • Ilmumisaeg: 19-Jul-2011
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1118161882
  • ISBN-13: 9781118161883
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118161883
"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 arediscussed. The book concludes with appendices which provide general descriptions of several evolutionary algorithms"--

Provided by publisher.

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.
Preface xiii
List of Contributors
xvii
1 Production Planning Using Genetic Algorithm
1(18)
S.K. Kumar
M. K. Tiwari
1.1 Introduction
1(1)
1.2 Production Planning Models
2(7)
1.2.1 Mathematical Model
3(6)
1.3 Genetic Algorithm
9(6)
1.3.1 Procedure of Genetic Algorithm (GA)
10(5)
1.4 Implementation of GA
15(3)
1.4.2 Parameter Tuning
16(2)
1.5 Summary
18(1)
Further Reading
18(1)
2 Process Planning through Ant Colony Optimization
19(18)
Puneet Bhardwaj
M. K. Tiwari
2.1 Introduction
19(6)
2.2 Ant Colony Optimization (ACO)
25(12)
2.2.1 Problem Description
27(1)
2.2.2 Case Problem
28(3)
2.2.3 Results
31(2)
References
33(4)
3 Introducing a Hybrid Genetic Algorithm for Integration of Set Up and Process Planning
37(14)
S. H. Chung
F. T. S. Chan
3.1 Introduction
38(1)
3.2 Process Planning
38(1)
3.3 Machine Set-up Time
39(4)
3.3.1 Optimization Methodology: Genetic Algorithms (GA)
41(2)
3.4 Chromosome Representation
43(1)
3.5 Fitness Value Evaluation
44(1)
3.6 Selection Operation
45(2)
3.7 Crossover Operations
47(1)
3.8 Mutation Operations (k-opt exchange)
47(1)
3.9 Conclusion
48(3)
References
48(3)
4 Design for Supply Chain with Product Development Issues Using Cellular Particle Swarm Optimization (CPSO) Technique
51(26)
Vikas Kumar
F. T. S. Chan
4.1 Introduction
52(3)
4.2 Problem Formulation
55(16)
4.2.1 Notations
56(4)
4.2.2 Simulated Problem
60(3)
4.2.3 Particle Swarm Algorithm (PSO)
63(4)
4.2.4 Cellular Particle Swarm Optimization (CPSO) Algorithm
67(2)
4.2.5 CPSO-outer Algorithm
69(2)
4.3 Computational Analysis and Result
71(3)
4.4 Conclusions
74(3)
References
75(2)
5 Genetic Algorithms with Chromosome Differentiation (GACD) Based Approach for Process Plan Selection Problems
77(18)
Nishikant Mishra
Vikas Kumar
5.1 Introduction
77(3)
5.2 Problem Formulation
80(1)
5.3 Genetic Algorithm with Chromosome Differentiation
81(5)
5.3.1 Overview of GA
81(1)
5.3.2 Genetic Algorithm Incorporating Chromosome Differentiation
82(1)
5.3.3 Description of GA with Chromosome Differentiation
82(4)
5.4 GACD Based Solution Methodology to Process Plan Selection Problem
86(4)
5.4.1 Selection of GACD's Parameter
90(1)
5.5 Numerical Experiments
90(2)
5.6 Conclusions
92(3)
References
92(3)
6 Operation Allocation in Flexible Manufacturing System Using Immune Algorithm
95(28)
Mayank K. Pandey
6.1 Introduction
96(4)
6.2 Machine Loading Problem
100(6)
6.2.1 Problem Formulation
103(3)
6.3 Solution Methodology
106(7)
6.3.1 Introduction to Immune System and Analogy to Immune Algorithm
106(2)
6.3.2 Modified Immune Algorithm Used to Solve Machine Loading Problem (Prakash et al. 2008)
108(5)
6.3.3 Fast Clonal Algorithm (Khilwani et al., 2008)
113(1)
6.4 Implementing Immune Algorithm for Machine Loading Problem
113(1)
6.5 Computational Result
114(3)
6.6 Conclusion
117(6)
References
119(4)
7 Tool Selection in FMS A Hybrid SA-Tabu Algorithm Based Approach
123(28)
Nitesh Khilwani
J. A. Harding
Nishikant Mishra
7.1 Introduction
124(1)
7.2 Literature Survey
125(2)
7.3 Problem Formulation
127(3)
7.4 Background on SA-Tabu Heuristic
130(3)
7.4.1 Simulated Annealing
130(1)
7.4.2 Tabu Search
131(2)
7.4.3 Simulated Annealing-Tabu
133(1)
7.5 Implementation of Tabu-Simulated Annealing
133(6)
7.5.1 Notations Used in SA-Tabu Heuristic
133(1)
7.5.2 Steps of the Hybrid SA-Tabu Heuristic
134(1)
7.5.3 Representation
135(1)
7.5.4 Search Parameters
136(3)
7.6 Test Cases
139(5)
7.7 Conclusion
144(7)
References
148(3)
8 Integrating AGVs and Production Planning with Memetic Particle Swarm Optimization
151(18)
Sri Krishna Kumar
M. K. Tiwari
J. Harding
8.1 Introduction
151(3)
8.1.1 Production and AGVs Scheduling
153(1)
8.1.2 AGVs Routing
154(1)
8.2 Literature Review
154(1)
8.3 Mathematical Model
155(4)
8.3.1 Problem Statement
155(1)
8.3.2 Mathematical Programming Model
155(4)
8.4 PSO and EMPSO
159(2)
8.5 Example
161(2)
8.6 Recombination (Local Search)
163(3)
8.7 Summary
166(3)
References
166(3)
9 Simulation-Based Aircraft Assembly Planning Using a Self-Guided Ant Colony Algorithm
169(28)
Sai Srinivas Nageshwaraniyer
Nurcin Celik
Young-Jun Son
Roberto Lu
9.1 Introduction
170(2)
9.2 Background and Literature Survey
172(5)
9.2.1 Assembly Planning in Aircraft Manufacturing
172(4)
9.2.2 Self-Guided Ant Colony Algorithm
176(1)
9.3 Specifications of the Considered Aircraft Assembly
177(2)
9.4 Proposed Simulation-Based Assembly Planning Framework
179(10)
9.4.1 Overview of the Proposed Framework
179(4)
9.4.2 Mathematical Formulation
183(1)
9.4.3 Details of Self Guided Ant Colony Algorithm (SGAC)
184(5)
9.5 Experiment and Results
189(3)
9.5.1 Effect of Rework on the Total Lead Time
191(1)
9.5.2 Effect of Size of the Order on the Average Utilization of Workstations
192(1)
9.6 Conclusion and Future Work
192(5)
References
193(4)
10 Applications of Evolutionary Computing to Additive Manufacturing
197(38)
Candice Majewski
10.1 Introduction
198(2)
10.2 Design for Additive Manufacturing
200(12)
10.2.1 Structural Design
200(3)
10.2.2 Functional Grading
203(2)
10.2.3 Digital Design/Art
205(3)
10.2.4 Inspired by Nature
208(2)
10.2.5 Future Challenges
210(2)
10.3 Data Handling
212(4)
10.4 Process Planning
216(16)
10.4.1 Build Packing
216(7)
10.4.2 Part Orientation
223(3)
10.4.3 Slicing
226(3)
10.4.4 Parameter Optimisation
229(2)
10.4.5 Summary
231(1)
10.5 Concluding Remarks
232(3)
References
232(3)
11 Multiple Fault Diagnosis Using Psycho-Clonal Algorithms
235(24)
Nagesh Shukla
P.K.S. Prakash
11.1 Introduction
235(2)
11.2 Multiple Fault Diagnosis Problems
237(5)
11.3 Background of Psychoclonal Algorithm
242(8)
11.3.1 Artificial Immune System (AIS)
242(2)
11.3.2 Theory of Clonal Selection
244(2)
11.3.3 Maslow's Need Hierarchy Theory
246(2)
11.3.4 Pseudo Code for Psycho Clonal Algorithm
248(2)
11.4 Numerical Experiments
250(4)
11.4.1 Test Problems
250(2)
11.4.2 Results and Discussions
252(2)
11.5 Conclusion
254(5)
References
257(2)
12 Platform Formation Under Stochastic Demand
259(30)
D. Ben-Arieh
A. M. Choubey
12.1 Introduction
259(2)
12.2 Background
261(2)
12.3 Problem Description
263(5)
12.3.1 Problem Statement
264(1)
12.3.2 Formulation of the Model
265(3)
12.4 Evolutionary Solution Approaches
268(4)
12.4.1 Solution Encoding
269(1)
12.4.2 Genetic Algorithm with Integer Programming (GAIP)
269(2)
12.4.3 Pure Probability Based Heuristic Approach
271(1)
12.4.4 Extension to Independent Demand for Each Product
272(1)
12.5 Example Problem - Results and Discussions
272(11)
12.5.1 Example
272(1)
12.5.2 Results and Discussions
273(1)
12.5.3 Results and Analysis Using GAIP
273(2)
12.5.4 The Solution Quality of PHA and Comparison with the GAIP Approach
275(5)
12.5.5 Results When Demand of Each Product is Represented as a Probability Distribution
280(3)
12.6 Conclusion and Recommendations for Future Research
283(6)
References
285(4)
13 A Hybrid Particle Swarm and Ant Colony Optimizer for Multi-attribute Partnership Selection in Virtual Enterprises
289(38)
S. H. Niu
S. K. Ong
A. Y. C. Nee
13.1 Introduction
289(3)
13.2 Literature Review
292(2)
13.3 Partner Selection Problem Formation
294(3)
13.3.1 Fundamental Variables Discussion
294(1)
13.3.2 Partner Selection Problem Description
295(2)
13.4 Solution Methodology
297(11)
13.4.1 Particle Swarm Optimization
297(2)
13.4.2 Ant Colony Optimization
299(1)
13.4.3 Hybrid PSO-ACO
300(3)
13.4.4 Weights of the Criteria and the Qualitative Variables
303(5)
13.5 Experimental Analysis
308(11)
13.5.1 Determine the Weights of the Main Criteria and Sub-Criteria
309(4)
13.5.2 Evaluation of Qualitative Attributes
313(3)
13.5.3 Evaluation of the Quantitative Aspects of the Enterprise
316(1)
13.5.4 Results
316(3)
13.6 Conclusion
319(8)
Nomenclature
320(4)
References
324(3)
Index 327
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.
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