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4j u oo Contents,Preface xiii,List of Contributors xvii. 1 Production Planning Using,Genetic Algorithm 1,S K Kumar and M K Tizvari. 1 1 Introduction 1,1 2 Production Planning Models 2. 1 2 1 Mathematical Model 3,1 3 Genetic Algorithm 9. 1 3 1 Procedure of Genetic Algorithm GA 10,1 4 Implementation of GA 15.
1 4 2 Parameter Tuning 16,1 5 Summary 18,Further Reading 18. 2 Process Planning through Ant Colony Optimization 19. Puneet Bhardwaj and M K Tiwari,2 1 Introduction 19. 2 2 Ant Colony Optimization ACO 25,2 2 1 Problem Description 27. 2 2 2 Case Problem 28,2 2 3 Results 31,References 33. 3 Introducing a Hybrid Genetic Algorithm for,Integration of Set Up and Process Planning 37.
S H Chung and F T S Chan,3 1 Introduction 38,3 2 Process Planning 38. 3 3 Machine Set up Time 39,3 3 1 Optimization Methodology. Genetic Algorithms GA 41,3 4 Chromosome Representation 43. 3 5 Fitness Value Evaluation 44,3 6 Selection Operation 45. 3 7 Crossover Operations 47,vi CONTENTS,3 8 Mutation Operations k opt exchange 47.
3 9 Conclusion 48,References 48,4 Design for Supply Chain with Product. Development Issues Using Cellular Particle,Swarm Optimization CPSO Technique 51. Vikas Kumar and F T S Chan,4 1 Introduction 52,4 2 Problem Formulation 55. 4 2 1 Notations 56,4 2 2 Simulated Problem 60,4 2 3 Particle Swarm Algorithm PSO 63. 4 2 4 Cellular Particle Swarm Optimization,CPSO Algorithm 67.
4 2 5 CPSO outer Algorithm 69,4 3 Computational Analysis and Result 71. 4 4 Conclusions 74,References 75,5 Genetic Algorithms with Chromosome. Differentiation GACD Based Approach,for Process Plan Selection Problems 77. Nishikant Mishra and Vikas Kumar,5 1 Introduction 77. 5 2 Problem Formulation 80,5 3 Genetic Algorithm with Chromosome.
Differentiation 81,5 3 1 Overview of GA 81,5 3 2 Genetic Algorithm Incorporating. Chromosome Differentiation 82,5 3 3 Description of GA with Chromosome. Differentiation 82,5 4 GACD Based Solution Methodology to. Process Plan Selection Problem 86,5 4 1 Selection of GACD s Parameter 90. 5 5 Numerical Experiments 90,5 6 Conclusions 92,References 92.
CONTENTS vii,6 Operation Allocation in Flexible Manufacturing. System Using Immune Algorithm 95,Mayank K Pandey,6 1 Introduction 96. 6 2 Machine Loading Problem 100,6 2 1 Problem Formulation 103. 6 3 Solution Methodology 106,6 3 1 Introduction to Immune System and. Analogy to Immune Algorithm 106,6 3 2 Modified Immune Algorithm Used.
to Solve Machine Loading Problem,Prakash et al 2008 108. 6 3 3 Fast Clonal Algorithm,Khilwani et al 2008 113. 6 4 Implementing Immune Algorithm for,Machine Loading Problem 113. 6 5 Computational Result 114,6 6 Conclusion 117,References 119. 7 Tool Selection in FMS A Hybrid SA Tabu,Algorithm Based Approach 123.
Nitesh Khilwani jlA Harding and Nishikant Mishra,7 1 Introduction 124. 7 2 Literature Survey 125,7 3x Problem Formulation 127. 7 4 Background on SA Tabu Heuristic 130,7 4 1 Simulated Annealing 130. 7 4 2 Tabu Search 131,7 4 3 Simulated Annealing Tabu 133. 7 5 Implementation of Tabu Simulated Annealing 133. 7 5 1 Notations Used in SA Tabu Heuristic 133,7 5 2 Steps of the Hybrid SA Tabu Heuristic 134.
7 5 3 Representation 135,7 5 4 Search Parameters 136. 7 6 Test Cases 139,7 7 Conclusion 144,References 148. viii CONTENTS,8 Integrating AGVs and Production Planning. with Memetic Particle Swarm Optimization 151,Sri Krishna Kumar M K Tiwari and J Harding. 8 1 Introduction 151,8 1 1 Production and AGVs Scheduling 153.
8 1 2 AGVs Routing 154,8 2 Literature Review 154,8 3 Mathematical Model 155. 8 3 1 Problem Statement 155,8 3 2 Mathematical Programming Model 155. 8 4 PSOandEMPSO 159,8 5 Example 161,8 6 Recombination Local Search 163. 8 7 Summary 166,References 166,9 Simulation Based Aircraft Assembly Planning. Using a Self Guided Ant Colony Algorithm 169,Sai Srinivas Nageshwaraniyer Nurcin Celik.
Young Jun Son and Roberto Lu,9 1 Introduction 170,9 2 Background and Literature Survey 172. 9 2 1 Assembly Planning in Aircraft,Manufacturing 172. 9 2 2 Self Guided Ant Colony Algorithm 176,9 3 Specifications of the Considered. Aircraft Assembly 177,9 4 Proposed Simulation Based Assembly. Planning Framework 179,9 4 1 Overview of the Proposed.
Framework I 179,9 4 2 Mathematical Formulation 183. 9 4 3 Details of Self Guided Ant Colony,Algorithm SGAC 184. 9 5 Experiment and Results 189,9 5 1 Effect of Rework on the Total Lead Time 191. 9 5 2 Effect of Size of the Order on the Average,Utilization of Workstations 192. 9 6 Conclusion and Future Work 192,References 193,CONTENTS ix.
10 Applications of Evolutionary Computing to,Additive Manufacturing 197. Candice Majewski,10 1 Introduction 198,10 2 Design for Additive Manufacturing 200. 10 2 1 Structural Design 200,10 2 2 Functional Grading 203. 10 2 3 Digital Design Art 205,10 2 4 Inspired by Nature 208. 10 2 5 Future Challenges 210,10 3 Data Handling 212.
10 4 Processi Planning 216,10 4 1 Build Packing 216. 10 4 2 Part Orientation 223,10 4 3 Slicing 226,10 4 4 Parameter Optimisation 229. 10 4 5 Summary 231,10 5 Concluding Remarks 232,References 232. 11 Multiple Fault Diagnosis Using Psycho Clonal,Algorithms 235. Nagesh Shukla and PKS Prakash,11 1 Introduction 235.
11 2 Multiple Fault Diagnosis Problems 237,11 3 Background of Psychoclonal Algorithm 242. 11 3 1 Artificial Immune System AIS 242,11 3 2 Theory of Clonal Selection 244. 11 3 3 Maslow s Need Hierarchy Theory 246,11 3 4 Pseudo Code for Psycho. Clonal Algorithm 248,11 4 Numerical Experiments 250. 11 4 1 Test Problems 250,11 4 2 Results and Discussions 252.
11 5 Conclusion 254,References 257,12 Platform Formation Under Stochastic Demand 259. D Ben Arieh and A M Choubey,12 1 Introduction 259,12 2 Background 261. x CONTENTS,12 3 Problem Description 263,12 3 1 Problem Statement 264. 12 3 2 Formulation of the Model 265,12 4 Evolutionary Solution Approaches 268. 12 4 1 Solution Encoding 269,12 4 2 Genetic Algorithm with Integer.
Programming GAIP 269,12 4 3 Pure Probability Based. Heuristic Approach 271,12 4 4 Extension to Independent Demand for. Each Product 272,12 5 Example Problem Results and Discussions 272. 12 5 1 Example 272,12 5 2 Results and Discussions 273. 12 5 3 Results and Analysis Using GAIP 273,12 5 4 The Solution Quality of PHA and.
Comparison with the GAIP Approach 275,12 5 5 Results When Demand of Each. Product is Represented as a,Probability Distribution 280. 12 6 Conclusion and Recommendations for,Future Research 283. References 285,13 A Hybrid Particle Swarm and Ant Colony. Optimizer for Multi attribute Partnership,Selection in Virtual Enterprises 289.
S H Niu S K Ong and A Y C Nee,13 1 Introduction 289. 13 2 Literature Review 292,13 3 Partner Selection Problem Formation 294. 13 3 1 Fundamental Variables Discussion 294,13 3 2 Partner Selection Problem Description 295. 13 4 Solution Methodology 297,13 4 1 Particle Swarm Optimization 297. 13 4 2 Ant Colony Optimization 299,13 4 3 Hybrid PSO ACO 300.
13 4 4 Weights of the Criteria and the,Qualitative Variables 303. 13 5 Experimental Analysis 308,13 5 1 Determine the Weights of the. Main Criteria and Sub Criteria 309,CONTENTS xi,13 5 2 Evaluation of Qualitative Attributes 313. 13 5 3 Evaluation of the Quantitative,Aspects of the Enterprise 316. 13 5 4 Results 316,13 6 Conclusion 319,Nomenclature 320.

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