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Emerging Optimization Techniques in Production Planning and Control / Godfrey C Onwubolu

Public ISBD Titre : Emerging Optimization Techniques in Production Planning and Control Type de document : texte imprimé Auteurs : Godfrey C Onwubolu, Auteur Editeur : London : Imperial College Press Année de publication : 2002 Importance : 632 p. Présentation : couv. ill. en coul., ill. Format : 22,8 cm. ISBN/ISSN/EAN : 978-1-86094-266-2 Langues : Anglais (eng) Catégories : ELECTROTECHNIQUE Index. décimale : 10-03 Machines éléctriques Résumé : This book proposes a concept of adaptive memory programming (AMP) for grouping a number of generic optimization techniques used in combinatorial problems. The same common features seen in the use of memory and a local search procedure drive these emerging optimization techniques, which include artificial neural networks, genetic algorithms, tabu search and ant systems. The primary motivation for AMP, therefore, is to group and unify all these techniques so as to enhance the computational capabilities that they offer for combinatorial problems encountered in real life in the area of production planning and control.The text describes the theoretical aspects of AMP together with relevant production planning and control applications. It covers the techniques, applications and algorithms. The book has been written in such a way that it can serve as an instructional text for students and those who are taking tuition on their own. The numerical examples given are first solved manually to enhance the reader's understanding of the material, and that is followed by a description of the algorithms and computer results. This way, the student can fully follow the material. The algorithms described for each application are useful to both students and practitioners in grasping how to implement similar applications in computer code using emerging optimization techniques. Note de contenu : Contents PART 1 Introduction Chapter 1 Introduction to Adaptive Memory Programming and Production Planning and Control 1.1. Production Planning Control within Integrated Manufacturing Framework 1.2. Conventional Combinatorial Optimization Techniques 1.3. Intelligent Optimization Fundamentals 1.4. Adaptive Memory Programming 1.5. Hybrid Systems 1.6. Summary PART 2 Production Planning and Control Decisions Chapter 2 Production Planning Systems 2.1. Introduction 2.2. Demand Forecasting 2.3. Production Planning 2.4. Master Production Schedule 2.5. Material Requirement Planning (MRP) 2.6. Resource Requirement Planning and Allocation 2.7. Rough Cut Capacity Planning (RCCP) 2.8. Capacity Resources Planning (CRP) 2.9. Summary Chapter 3 Production Control Systems 3.1. Introduction 3.2. Scheduling in Job Shop Production 3.3. Scheduling in Batch Production 3.4. Scheduling in Line Flow Production 3.5. Scheduling in Assembly Line Production 3.6. Material Management 3.7. Inventory Control 3.8. Inventory Control Systems 3.9. Quality Control 3.10. Summary PART 3 Emerging Optimization Techniques Chapter 4 Artificial Neural Networks 4.1. Background to Neural Networks 4.2. Learning in Supervised Neural Networks: Delta Rule 4.3. Backpropagation Neural Network (BPN) 4.4. Self-Organising Map (SOM) Neural Network 4.5. Adaptive Resonance Theory 4.6. Hopfield Neural Network 4.7. Application of Neural Networks to Machine Tooling and Production Sequencing in Manufacturing Ce 4.8. Summary Chapter 5 Genetic Algorithms 5.1. Introduction 5.2. Fundamentals of Genetic Algorithms 5.3. Manual Simulation of Genetic Algorithms 5.4. Aggregate Production Planning 5.5. Genetic Algorithms Design Issues 5.6. Genetic Algorithm Implementation 5.7. Qualitative Innovations and Improvements 5.8. Computational Tests and Results 5.8.1. Comparing genetic algorithms with other methods 5.8.2. Comparing genetic algorithms with integer linear programming 5.9. Summary Chapter 6 Tabu Search 6.1. Background to Tabu Search 6.2. The Dilemma of Hill Climbing 6.3. Tabu Search Fundamentals 6.4. Short Term Memory in Tabu Search 6.5. Long Term Memory in Tabu Search 6.6. The Theory of Constraints Product Mix Problem 6.7. Application of Tabu Search to the Product Mix Problem 6.8. Summary Chapter 7 Ant Systems 7.1. The Ant System Paradigm 7.2. Ant Systems Fundamentals 7.3. FANT: Fast Ant System 7.4. HAS: Hybrid Ant System 7.5. The FANT Simulator 7.6. HAS Simulator 7.7. Application of FANT to Flow Shop Scheduling: 1-Criterion 7.8. Application of FANT to Flow Shop Scheduling: Bi-Criteria 7.9. Summary Chapter 8 Simulated Annealing 8.1. Simulated Annealing Paradigm 8.2. Monte Carlo Model for Simulating Physical Annealing 8.3. Analogy Between Physical and Simulated Annealing 8.4. Cooling Schedule Classifications for Simulated Annealing Schemes 8.5. Neighbourhood Search Techniques 8.6. Production Layout Strategies 8.7. Production Layout Planning 8.8. Application of Simulated Annealing to Cell Formation 8.9. Summary Chapter 9 Programming Techniques 9.1. Data Structure 9.2. Modular Design 9.3. Simple Tabu Search Run 9.4. Summary Appendix A. Pascal Fundamentals A.1. Putting Pascal fundamentals to use A.2. Getting something from Pascal fundamentals A.3. Summary B. A Simple Tabu Search in Pascal Author Index Subject Index Emerging Optimization Techniques in Production Planning and Control [texte imprimé] / Godfrey C Onwubolu, Auteur . - London : Imperial College Press, 2002 . - 632 p. : couv. ill. en coul., ill. ; 22,8 cm. ISBN : 978-1-86094-266-2 Langues : Anglais (eng) Catégories : ELECTROTECHNIQUE Index. décimale : 10-03 Machines éléctriques Résumé : This book proposes a concept of adaptive memory programming (AMP) for grouping a number of generic optimization techniques used in combinatorial problems. The same common features seen in the use of memory and a local search procedure drive these emerging optimization techniques, which include artificial neural networks, genetic algorithms, tabu search and ant systems. The primary motivation for AMP, therefore, is to group and unify all these techniques so as to enhance the computational capabilities that they offer for combinatorial problems encountered in real life in the area of production planning and control.The text describes the theoretical aspects of AMP together with relevant production planning and control applications. It covers the techniques, applications and algorithms. The book has been written in such a way that it can serve as an instructional text for students and those who are taking tuition on their own. The numerical examples given are first solved manually to enhance the reader's understanding of the material, and that is followed by a description of the algorithms and computer results. This way, the student can fully follow the material. The algorithms described for each application are useful to both students and practitioners in grasping how to implement similar applications in computer code using emerging optimization techniques. Note de contenu : Contents PART 1 Introduction Chapter 1 Introduction to Adaptive Memory Programming and Production Planning and Control 1.1. Production Planning Control within Integrated Manufacturing Framework 1.2. Conventional Combinatorial Optimization Techniques 1.3. Intelligent Optimization Fundamentals 1.4. Adaptive Memory Programming 1.5. Hybrid Systems 1.6. Summary PART 2 Production Planning and Control Decisions Chapter 2 Production Planning Systems 2.1. Introduction 2.2. Demand Forecasting 2.3. Production Planning 2.4. Master Production Schedule 2.5. Material Requirement Planning (MRP) 2.6. Resource Requirement Planning and Allocation 2.7. Rough Cut Capacity Planning (RCCP) 2.8. Capacity Resources Planning (CRP) 2.9. Summary Chapter 3 Production Control Systems 3.1. Introduction 3.2. Scheduling in Job Shop Production 3.3. Scheduling in Batch Production 3.4. Scheduling in Line Flow Production 3.5. Scheduling in Assembly Line Production 3.6. Material Management 3.7. Inventory Control 3.8. Inventory Control Systems 3.9. Quality Control 3.10. Summary PART 3 Emerging Optimization Techniques Chapter 4 Artificial Neural Networks 4.1. Background to Neural Networks 4.2. Learning in Supervised Neural Networks: Delta Rule 4.3. Backpropagation Neural Network (BPN) 4.4. Self-Organising Map (SOM) Neural Network 4.5. Adaptive Resonance Theory 4.6. Hopfield Neural Network 4.7. Application of Neural Networks to Machine Tooling and Production Sequencing in Manufacturing Ce 4.8. Summary Chapter 5 Genetic Algorithms 5.1. Introduction 5.2. Fundamentals of Genetic Algorithms 5.3. Manual Simulation of Genetic Algorithms 5.4. Aggregate Production Planning 5.5. Genetic Algorithms Design Issues 5.6. Genetic Algorithm Implementation 5.7. Qualitative Innovations and Improvements 5.8. Computational Tests and Results 5.8.1. Comparing genetic algorithms with other methods 5.8.2. Comparing genetic algorithms with integer linear programming 5.9. Summary Chapter 6 Tabu Search 6.1. Background to Tabu Search 6.2. The Dilemma of Hill Climbing 6.3. Tabu Search Fundamentals 6.4. Short Term Memory in Tabu Search 6.5. Long Term Memory in Tabu Search 6.6. The Theory of Constraints Product Mix Problem 6.7. Application of Tabu Search to the Product Mix Problem 6.8. Summary Chapter 7 Ant Systems 7.1. The Ant System Paradigm 7.2. Ant Systems Fundamentals 7.3. FANT: Fast Ant System 7.4. HAS: Hybrid Ant System 7.5. The FANT Simulator 7.6. HAS Simulator 7.7. Application of FANT to Flow Shop Scheduling: 1-Criterion 7.8. Application of FANT to Flow Shop Scheduling: Bi-Criteria 7.9. Summary Chapter 8 Simulated Annealing 8.1. Simulated Annealing Paradigm 8.2. Monte Carlo Model for Simulating Physical Annealing 8.3. Analogy Between Physical and Simulated Annealing 8.4. Cooling Schedule Classifications for Simulated Annealing Schemes 8.5. Neighbourhood Search Techniques 8.6. Production Layout Strategies 8.7. Production Layout Planning 8.8. Application of Simulated Annealing to Cell Formation 8.9. Summary Chapter 9 Programming Techniques 9.1. Data Structure 9.2. Modular Design 9.3. Simple Tabu Search Run 9.4. Summary Appendix A. Pascal Fundamentals A.1. Putting Pascal fundamentals to use A.2. Getting something from Pascal fundamentals A.3. Summary B. A Simple Tabu Search in Pascal Author Index Subject Index

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An Introduction To The Principles Of Medical Imaging / Chris Guy

Public ISBD Titre : An Introduction To The Principles Of Medical Imaging Type de document : texte imprimé Auteurs : Chris Guy, Auteur ; Dominic Ffytche, Auteur Editeur : London : Imperial College Press Année de publication : 2005 Importance : 374 p. Présentation : couv. ill. en coul., ill. Format : 23,5 cm. ISBN/ISSN/EAN : 978-1-86094-502-1 Langues : Anglais (eng) Catégories : GÉNIE BIOMÉDICAL Index. décimale : 35-03 Traitement d'images médicales Résumé : The introduction of X-ray computed tomography (CT) 25 years ago revolutionized medical imaging; X-ray CT itself provided the first clear cross-sectional images of the human body, with substantial contrast between different types of soft tissue. The enduring legacy of CT is, however, the spur that it gave to the subsequent introduction of tomographic imaging techniques into diagnostic nuclear medicine and the extraordinarily rapid development of magnetic resonance imaging (MRI) over this period.This book is a non-mathematical introduction to the principles underlying modern medical imaging, taking tomography as its central theme. The first three chapters cover the general principles of tomography, a survey of the atomic and nuclear physics which underpins modern imaging, and a review of the key issues involved in radiation protection. The subsequent chapters deal in turn with X-ray radiography, gamma imaging, MRI and ultrasound. The clinical role of diagnostic imaging is illustrated in the final chapter through the use of fictional clinical histories. Three appendices provide a more mathematical background to the tomographic method, the principles of mathematical Fourier methods, and the mathematics of MRI.This revised edition includes new introductory sections on the relevant physics of molecules in general, and water, in particular. Every chapter now has a table of key points with cross-references to other sections. Several figures have also been revised.The book is intended to provide a broad introductory background to tomographic imaging for two groups of readers: the physics or engineering undergraduate thinking of specializing in medical physics, and the medical student or clinician using tomographic techniques in research and clinical practice. Note de contenu : Contents: -Introduction 1 Tomography 2 Atomic and Nuclear Physics 3 Radiation Protection 4 X-ray Radiography 5 Nuclear Medicine: Gamma Imaging 6 Magnetic Resonance Imaging 7 Ultrasound Imaging 8 Imaging in Clinical Practice Appendix A: Waves, Images and Fourier analysis Appendix B: Fourier Image Reconstruction Appendix C: Mathematics of NMR -Index An Introduction To The Principles Of Medical Imaging [texte imprimé] / Chris Guy, Auteur ; Dominic Ffytche, Auteur . - London : Imperial College Press, 2005 . - 374 p. : couv. ill. en coul., ill. ; 23,5 cm. ISBN : 978-1-86094-502-1 Langues : Anglais (eng) Catégories : GÉNIE BIOMÉDICAL Index. décimale : 35-03 Traitement d'images médicales Résumé : The introduction of X-ray computed tomography (CT) 25 years ago revolutionized medical imaging; X-ray CT itself provided the first clear cross-sectional images of the human body, with substantial contrast between different types of soft tissue. The enduring legacy of CT is, however, the spur that it gave to the subsequent introduction of tomographic imaging techniques into diagnostic nuclear medicine and the extraordinarily rapid development of magnetic resonance imaging (MRI) over this period.This book is a non-mathematical introduction to the principles underlying modern medical imaging, taking tomography as its central theme. The first three chapters cover the general principles of tomography, a survey of the atomic and nuclear physics which underpins modern imaging, and a review of the key issues involved in radiation protection. The subsequent chapters deal in turn with X-ray radiography, gamma imaging, MRI and ultrasound. The clinical role of diagnostic imaging is illustrated in the final chapter through the use of fictional clinical histories. Three appendices provide a more mathematical background to the tomographic method, the principles of mathematical Fourier methods, and the mathematics of MRI.This revised edition includes new introductory sections on the relevant physics of molecules in general, and water, in particular. Every chapter now has a table of key points with cross-references to other sections. Several figures have also been revised.The book is intended to provide a broad introductory background to tomographic imaging for two groups of readers: the physics or engineering undergraduate thinking of specializing in medical physics, and the medical student or clinician using tomographic techniques in research and clinical practice. Note de contenu : Contents: -Introduction 1 Tomography 2 Atomic and Nuclear Physics 3 Radiation Protection 4 X-ray Radiography 5 Nuclear Medicine: Gamma Imaging 6 Magnetic Resonance Imaging 7 Ultrasound Imaging 8 Imaging in Clinical Practice Appendix A: Waves, Images and Fourier analysis Appendix B: Fourier Image Reconstruction Appendix C: Mathematics of NMR -Index

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Uncertainty and feedback / Glenn Vinnicombe

Public ISBD Titre : Uncertainty and feedback : h loop-shaping and the v-gap metric Type de document : texte imprimé Auteurs : Glenn Vinnicombe, Auteur Editeur : London : Imperial College Press Année de publication : 2001 Importance : 316 p. Présentation : couv. ill. en coul., ill. Format : 21,8 cm. ISBN/ISSN/EAN : 978-1-86094-163-4 Langues : Anglais (eng) Catégories : AUTOMATISME Index. décimale : 25-04 Théorie des systèmes:systèmes asservis Résumé : The principal reason for using feedback is to reduce the effect of uncertainties in the description of a system which is to be controlled. H∞ loop-shaping is emerging as a powerful but straightforward method of designing robust feedback controllers for complex systems. However, in order to use this, or other modern design techniques, it is first necessary to generate an accurate model of the system (thus appearing to remove the reason for needing feedback in the first place). The ν-gap metric is an attempt to resolve this paradox -- by indicating in what sense a model should be accurate if it is to be useful for feedback design.This book develops in detail the H∞ loop-shaping design method, the ν-gap metric and the relationship between the two, showing how they can be used together for successful feedback design. Note de contenu : 1 An introduction to H∞, control 1.1 Norms on signals and systems 1.2 The stability of feedback systems 1.3 Robust stability 1.4 Solution to the H∞ control problem 1.5 Graphs of linear systems* 2 H∞ Loop-shaping 2.1 Introduction 2.2 The loop shaping design procedure of McFarlane and Glover 2.3 Inner functions, and properties of bp,c 2.4 bp,c and the Riemann sphere 2.5 Direct bounds on the closed-loop transfer functions 2.6 Choosing the weights, Part 1: For performance 3 The v-gap metric 3.1 Introduction 3.2 The v-gap metric 3.3 Robust stability and performance theorems 3.4 Parametric uncertainty and the v-gap metric 3.5 Choosing the weights , Part 2: Forrobustness 3.6 Closed-loop errors and the v-gap 3.7 Extending the metric 3.8 The L2-gap and the graph topology CHAPTER 4 More H∞ loop-shaping 4.1 The optimal controller, and the optimal stability margin 4.2 A frequency response interpretation of bopt(P) 4.3 Choosing the weights , Part 3: For feasibility 4.4 An extended loop shaping design procedure 4.5 Robust tracking CHAPTER 5 Complexity and robustness 5.1 Introduction 5.2 Less conservative robustness results 5.3 Examples 5.4 The discrete time case 5.5 Complexity definitions CHAPTER 6 Design examples 6.1 A benchmark design example 6.2 Complexity based design example CHAPTER 7 Topologies, metrics and operator theory 7.1 Topologies and metrics 7.2 Operator theory, and the non-rational case 7.3 Comparison with the gap metric 7.4 Examples CHAPTER 8 Approximation in the graph topology 8.1 The nearest non-stabilizable plant 8.2 Upper and lower bounds for approximation CHAPTER 9 The best possible H∞ robustness results 9.1 Introduction 9.2 Results based on homotopy arguments 9.3 Additive and normalized coprime factoruncertainty 9.4 Alternative characterizations of the set P ( H , β) 9.5 Applications and examples APPENDIX A State-space formulae and proofs A.1 Existence of coprime factors A.2 Calculation of δv A.3 Observer based two degree of freedom compensators Index Uncertainty and feedback : h loop-shaping and the v-gap metric [texte imprimé] / Glenn Vinnicombe, Auteur . - London : Imperial College Press, 2001 . - 316 p. : couv. ill. en coul., ill. ; 21,8 cm. ISBN : 978-1-86094-163-4 Langues : Anglais (eng) Catégories : AUTOMATISME Index. décimale : 25-04 Théorie des systèmes:systèmes asservis Résumé : The principal reason for using feedback is to reduce the effect of uncertainties in the description of a system which is to be controlled. H∞ loop-shaping is emerging as a powerful but straightforward method of designing robust feedback controllers for complex systems. However, in order to use this, or other modern design techniques, it is first necessary to generate an accurate model of the system (thus appearing to remove the reason for needing feedback in the first place). The ν-gap metric is an attempt to resolve this paradox -- by indicating in what sense a model should be accurate if it is to be useful for feedback design.This book develops in detail the H∞ loop-shaping design method, the ν-gap metric and the relationship between the two, showing how they can be used together for successful feedback design. Note de contenu : 1 An introduction to H∞, control 1.1 Norms on signals and systems 1.2 The stability of feedback systems 1.3 Robust stability 1.4 Solution to the H∞ control problem 1.5 Graphs of linear systems* 2 H∞ Loop-shaping 2.1 Introduction 2.2 The loop shaping design procedure of McFarlane and Glover 2.3 Inner functions, and properties of bp,c 2.4 bp,c and the Riemann sphere 2.5 Direct bounds on the closed-loop transfer functions 2.6 Choosing the weights, Part 1: For performance 3 The v-gap metric 3.1 Introduction 3.2 The v-gap metric 3.3 Robust stability and performance theorems 3.4 Parametric uncertainty and the v-gap metric 3.5 Choosing the weights , Part 2: Forrobustness 3.6 Closed-loop errors and the v-gap 3.7 Extending the metric 3.8 The L2-gap and the graph topology CHAPTER 4 More H∞ loop-shaping 4.1 The optimal controller, and the optimal stability margin 4.2 A frequency response interpretation of bopt(P) 4.3 Choosing the weights , Part 3: For feasibility 4.4 An extended loop shaping design procedure 4.5 Robust tracking CHAPTER 5 Complexity and robustness 5.1 Introduction 5.2 Less conservative robustness results 5.3 Examples 5.4 The discrete time case 5.5 Complexity definitions CHAPTER 6 Design examples 6.1 A benchmark design example 6.2 Complexity based design example CHAPTER 7 Topologies, metrics and operator theory 7.1 Topologies and metrics 7.2 Operator theory, and the non-rational case 7.3 Comparison with the gap metric 7.4 Examples CHAPTER 8 Approximation in the graph topology 8.1 The nearest non-stabilizable plant 8.2 Upper and lower bounds for approximation CHAPTER 9 The best possible H∞ robustness results 9.1 Introduction 9.2 Results based on homotopy arguments 9.3 Additive and normalized coprime factoruncertainty 9.4 Alternative characterizations of the set P ( H , β) 9.5 Applications and examples APPENDIX A State-space formulae and proofs A.1 Existence of coprime factors A.2 Calculation of δv A.3 Observer based two degree of freedom compensators Index

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