| 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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