Industrial quality control Books

Book SynopsisDesigned for junior- and senior-level courses in plant and facilities planning and manufacturing systems and procedures, this textbook also is suitable for graduate-level and two-year college courses. The book takes a practical, hands-on, project-oriented approach to exploring the techniques and procedures for developing an efficient facility layout. It also introduces state-of-the-art tools including computer simulation. Access to Layout-iQ workspace planning software is included for purchasers of the book. Theoretical concepts are clearly explained and then rapidly applied to a practical setting through a detailed case study at the end of the volume. The book systematically leads students through the collection, analysis, and development of information to produce a quality functional plant layout for a lean manufacturing environment. All aspects of facility design, from receiving to shipping, are covered. In the sixth edition of this successful book, numerous updates and corrections have been made, and a chapter on engineering cost estimating and analysis has been added. Also, rather than including brief case-in-point examples at the end of each chapter, a single, detailed case study is provided that better exposes students to the multiple considerations that need to be taken into account when improving efficiency in a real manufacturing facility. The textbook has enjoyed substantial international adoptions and has been translated into Spanish and Chinese.Table of Contents CHAPTER 1: INTRODUCTION TO MANUFACTURING FACILITIES DESIGN AND MATERIAL HANDLING CHAPTER 2: SOURCES OF INFORMATION FOR MANUFACTURING FACILITIES DESIGN CHAPTER 3: TIME STUDY CHAPTER 4: PROCESS DESIGN CHAPTER 5: ACTIVITY RELATIONSHIP ANALYSIS CHAPTER 6: FLOW ANALYSIS TECHNIQUES CHAPTER 7: ERGONOMICS AND WORKSTATION DESIGN SPACE REQUIREMENTS CHAPTER 8: AUXILIARY SERVICES REQUIREMENT SPACE CHAPTER 9: EMPLOYEE SERVICES—SPACE REQUIREMENTS CHAPTER 10: MATERIAL HANDLING CHAPTER 11: MATERIAL HANDLING EQUIPMENT CHAPTER 12 OFFICE LAYOUT TECHNIQUES AND SPACE REQUIREMENTS CHAPTER 13: AREA ALLOCATION CHAPTER 14: FACILITIES DESIGN—THE LAYOUT CHAPTER 15: APPLICATION OF COMPUTER SIMULATION AND MODELING CHAPTER 16: ENGINEERING COST ESTIMATING AND ANALYSIS CHAPTER 17: SELLING THE LAYOUT ANSWERS APPENDIX INDEX

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Book SynopsisSplit into 4 accessible parts, the book presents: 1. An introduction to and definition of BBNs.2. Step-by-step practical guidelines to applying BBNs.3. A wide variety of applications in industry, natural sciences, services and computing.4. A discussion of the future directions BBN research and applications might take.Table of ContentsForeword ix Preface xi 1 Introduction to Bayesian networks 1 1.1 Models 1 1.2 Probabilistic vs. deterministic models 5 1.3 Unconditional and conditional independence 9 1.4 Bayesian networks 11 2 Medical diagnosis 15 2.1 Bayesian networks in medicine 15 2.2 Context and history 17 2.3 Model construction 19 2.4 Inference 26 2.5 Model validation 28 2.6 Model use 30 2.7 Comparison to other approaches 31 2.8 Conclusions and perspectives 32 3 Clinical decision support 33 3.1 Introduction 33 3.2 Models and methodology 34 3.3 The Busselton network 35 3.4 The PROCAM network 40 3.5 The PROCAM Busselton network 44 3.6 Evaluation 46 3.7 The clinical support tool: TakeHeartII 47 3.8 Conclusion 51 4 Complex genetic models 53 4.1 Introduction 53 4.2 Historical perspectives 54 4.3 Complex traits 56 4.4 Bayesian networks to dissect complex traits 59 4.5 Applications 64 4.6 Future challenges 71 5 Crime risk factors analysis 73 5.1 Introduction 73 5.2 Analysis of the factors affecting crime risk 74 5.3 Expert probabilities elicitation 75 5.4 Data preprocessing 76 5.5 A Bayesian network model 78 5.6 Results 80 5.7 Accuracy assessment 83 5.8 Conclusions 84 6 Spatial dynamics in France 87 6.1 Introduction 87 6.2 An indicator-based analysis 89 6.3 The Bayesian network model 97 6.4 Conclusions 109 7 Inference problems in forensic science 113 7.1 Introduction 113 7.2 Building Bayesian networks for inference 116 7.3 Applications of Bayesian networks in forensic science 120 7.4 Conclusions 126 8 Conservation of marbled murrelets in British Columbia 127 8.1 Context/history 127 8.2 Model construction 129 8.3 Model calibration, validation and use 136 8.4 Conclusions/perspectives 147 9 Classifiers for modeling of mineral potential 149 9.1 Mineral potential mapping 149 9.2 Classifiers for mineral potential mapping 151 9.3 Bayesian network mapping of base metal deposit 157 9.4 Discussion 166 9.5 Conclusions 171 10 Student modeling 173 10.1 Introduction 173 10.2 Probabilistic relational models 175 10.3 Probabilistic relational student model 176 10.4 Case study 180 10.5 Experimental evaluation 182 10.6 Conclusions and future directions 185 11 Sensor validation 187 11.1 Introduction 187 11.2 The problem of sensor validation 188 11.3 Sensor validation algorithm 191 11.4 Gas turbines 197 11.5 Models learned and experimentation 198 11.6 Discussion and conclusion 202 12 An information retrieval system 203 12.1 Introduction 203 12.2 Overview 205 12.3 Bayesian networks and information retrieval 206 12.4 Theoretical foundations 207 12.5 Building the information retrieval system 215 12.6 Conclusion 223 13 Reliability analysis of systems 225 13.1 Introduction 225 13.2 Dynamic fault trees 227 13.3 Dynamic Bayesian networks 228 13.4 A case study: The Hypothetical Sprinkler System 230 13.5 Conclusions 237 14 Terrorism risk management 239 14.1 Introduction 240 14.2 The Risk Influence Network 250 14.3 Software implementation 254 14.4 Site Profiler deployment 259 14.5 Conclusion 261 15 Credit-rating of companies 263 15.1 Introduction 263 15.2 Naive Bayesian classifiers 264 15.3 Example of actual credit-ratings systems 264 15.4 Credit-rating data of Japanese companies 266 15.5 Numerical experiments 267 15.6 Performance comparison of classifiers 273 15.7 Conclusion 276 16 Classification of Chilean wines 279 16.1 Introduction 279 16.2 Experimental setup 281 16.3 Feature extraction methods 285 16.4 Classification results 288 16.5 Conclusions 298 17 Pavement and bridge management 301 17.1 Introduction 301 17.2 Pavement management decisions 302 17.3 Bridge management 307 17.4 Bridge approach embankment – case study 308 17.5 Conclusion 312 18 Complex industrial process operation 313 18.1 Introduction 313 18.2 A methodology for Root Cause Analysis 314 18.3 Pulp and paper application 321 18.4 The ABB Industrial IT platform 325 18.5 Conclusion 326 19 Probability of default for large corporates 329 19.1 Introduction 329 19.2 Model construction 332 19.3 BayesCredit 335 19.4 Model benchmarking 341 19.5 Benefits from technology and software 342 19.6 Conclusion 343 20 Risk management in robotics 345 20.1 Introduction 345 20.2 DeepC 346 20.3 The ADVOCATE II architecture 352 20.4 Model development 354 20.5 Model usage and examples 360 20.6 Benefits from using probabilistic graphical models 361 20.7 Conclusion 362 21 Enhancing Human Cognition 365 21.1 Introduction 365 21.2 Human foreknowledge in everyday settings 366 21.3 Machine foreknowledge 369 21.4 Current application and future research needs 373 21.5 Conclusion 375 22 Conclusion 377 22.1 An artificial intelligence perspective 377 22.2 A rational approach of knowledge 379 22.3 Future challenges 384 Bibliography 385 Index 427

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Book SynopsisBased on deep theoretical as well as practical experience in Reliability and Quality Sciences, Robust Design Methodology for Reliability constructively addresses practical reliability problems. It offers a comprehensive design theory for reliability, utilizing robust design methodology and six sigma frameworks. In particular, the relation between un-reliability and variation and uncertainty is explored and reliability improvement measures in early product development stages are suggested. Many companies today utilise design for Six Sigma (DfSS) for strategic improvement of the design process, but often without explicitly describing the reliability perspective; this book explains how reliability design can relate to and work with DfSS and illustrates this with realworld problems. The contributors advocate designing for robustness, i.e. insensitivity to variation in the early stages of product design development. Methods for rational treatment of uncertainties in model assumptTable of ContentsPreface Acknowledgements About the Editors Contributors PART One METHODOLOGY 1 Introduction Bo Bergman and Martin Arvidsson 1.1 Background 1.2 Failure Mode Avoidance 1.3 Robust Design 1.4 Comments and Suggestions for Further Reading References 2 Evolution of Reliability Thinking – Countermeasures for Some Technical Issues Åke Lönnqvist 2.1 Introduction 2.2 Method 2.3 An Overview of the Initial Development of Reliability Engineering 2.4 Examples of Technical Issues and Reliability Countermeasures 2.5 Discussion and Future Research 2.6 Summary and Conclusions References 3 Principles of Robust Design Methodology Martin Arvidsson and Ida Gremyr 3.1 Introduction 3.2 Method 3.3 Results and Analysis 3.4 Discussion 3.5 Conclusions References PART Two METHODS 4 Including Noise Factors in Design Failure Mode and Effect Analysis (D-FMEA) – A Case Study at Volvo Car Corporation Åke Lönnqvist 4.1 Introduction 4.2 Background 4.3 Method 4.4 Result 4.5 Discussion and Further Research 4.6 Summary References 5 Robust Product Development Using Variation Mode and Effect Analysis Alexander Chakhunashvili, Stefano Barone, Per Johansson and Bo Bergman 5.1 Introduction 5.2 Overview of the VMEA Method 5.3 The Basic VMEA 5.4 The Enhanced VMEA 5.5 The Probabilistic VMEA 5.6 An Illustrative Example 5.7 Discussion and Concluding Remarks Appendix: Formal Justification of the VMEA Method References 6 Variation Mode and Effect Analysis: An Application to Fatigue Life Prediction Pär Johannesson, Thomas Svensson, Leif Samuelsson, Bo Bergman and Jacques de Maré 6.1 Introduction 6.2 Scatter and Uncertainty 6.3 A Simple Approach to Probabilistic VMEA 6.4 Estimation of Prediction Uncertainty 6.5 Reliability Assessment 6.6 Updating the Reliability Calculation 6.7 Conclusions and Discussion References 7 Predictive Safety Index for Variable Amplitude Fatigue Life Thomas Svensson, Jacques de Maré and Pär Johannesson 7.1 Introduction 7.2 The Load–Strength Reliability Method 7.3 The Equivalent Load and Strength Variables 7.4 Reliability Indices 7.5 The Gauss Approximation Formula 7.6 The Uncertainty Due to the Estimated Exponent β 7.7 The Uncertainty Measure of Strength 7.8 The Uncertainty Measure of Load 7.9 The Predictive Safety Index 7.10 Discussion Appendix References 8 Monte Carlo Simulation versus Sensitivity Analysis Sara Lorén, Pär Johannesson and Jacques de Mar´e 8.1 Introduction 8.2 Transfer Function 8.3 Example from an Industrial Context 8.4 Highly Nonlinear Transfer Function 8.5 Total Variation for Logarithmic Life 8.6 Conclusions References PART Three MODELLING 9 Model Complexity Versus Scatter in Fatigue Thomas Svensson 9.1 Introduction 9.2 A Statistical Model 9.3 Design Concepts 9.4 A Crack Growth Model 9.5 Partly Measurable Variables 9.6 Conclusions References 10 Choice of Complexity in Constitutive Modelling of Fatigue Mechanisms Erland Johnson and Thomas Svensson 10.1 Background 10.2 Questions 10.3 Method 10.4 Empirical Modelling 10.5 A Polynomial Example 10.6 A General Linear Formulation 10.7 A Fatigue Example References 11 Interpretation of Dispersion Effects in a Robust Design Context Martin Arvidsson, Ida Gremyr and Bo Bergman 11.1 Introduction 11.2 Dispersion Effects 11.3 Discussion References 12 Fatigue Damage Uncertainty Anders Bengtsson, Klas Bogsjöand Igor Rychlik 12.1 Introduction 12.2 Fatigue Review 12.3 Probability for Fatigue Failure – Safety Index 12.4 Computation of E [D(T )|k] and V [D(T )|k] 12.5 Non Gaussian Loads – Examples References 13 Widening the Perspectives Bo Bergman and Jacques de Maré 13.1 Background 13.2 Additional Engineering Perspectives on Reliability 13.3 Organizational Perspectives on Reliability 13.4 Industrialization of Robust Design Methodology 13.5 Adoptions of Fatigue Reliability Methodology 13.6 Learning for the Future References List of Abbreviations Index

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Book SynopsisThis volume provides basic guidelines concerning quality assurance and its responsibilities for biopharmaceuticals manufactured by either recombinant, monoclonal antibody or other biotechnological methodologies. Insight is given on the safety, purity and efficacy of the products produced.Table of ContentsOrganization of Quality Assurance (J. Huxsoll). Coming into GMP Compliance (C. Orelli). Documentation Systems (C. Brochard). Validation (D. Colton). Quality Assurance (QA) of Production Materials for Biotechnology(D. Miller). Quality Assurance (QA) of Analytical Methods--Biochemical (F.Bogdansky). Applied Data Analysis, Sampling Methodologies, and StatisticalValidation Techniques (G. Swartz). Environmental and Safety Programs for Biotechnology (M.Sigourney). Regulatory Issues--United States (C. Spencer). Regulatory Issues--Europe (R. Wikberg-Leonardi & D.Mulder). Regulatory Issues--Japan (R. Rousell & E. Greene). Abbreviations. Appendices. Index.

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Book SynopsisThis text presents an approach developed by the author, to handle some of the most common types of component imperfections encountered in industrial automation, consumer electronics, and defence and transportation systems.Table of ContentsDead-Zone, Backlash, and Hysteresis. Inverse Models. Fixed Inverse Compensation. Adaptive Inverse Examples. Continuous-Time Adaptive Inverse Control. Discrete-Time Adaptive Inverse Control. Fixed Inverse Control for Output Nonlinearities. Adaptive Inverse Control for Output Nonlinearities. Adaptive Control of Partially Known Systems. Adaptive Control with Input and Output Nonlinearities. Appendices. Bibliography. Index.

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Book SynopsisA comprehensive presentation of control theory for the SPC community Quality control has become a major concern in today's competitive industrial environment, and industrial engineers are constantly seeking to make process adjustments that will optimize production efficiency and improve product quality.Trade Review"It is definitely a book that I would gladly buy, and one that I would have no hesitation in recommending to fellow professionals." (IIE Transactions-Quality and Reliability Engineering, November 2005) "…I think that this is a great book that is well worth its price…from those working in the manufacturing area…this book is a valuable resource." (Journal of the American Statistical Association, June 2004) "...presents process adjustment techniques based on EPC methods and discusses them from the perspective of product quality control..." (SciTech Book News, Vol. 26, No. 2, June 2002) "...the author attempts consolidation of [controlling process variables and monitoring product attributes] along technical tools...this book successfully achieves the intended purpose…useful…easy-to-read..." (Mathematical Reviews, 2003a) "I like this book.... If you are a statistician interested in EPC or a process control engineer wondering how SPC relates to EPC, then this book is for you." (Technometrics, Vol. 45, No. 1, February 2003) "...I like this book very much and it should make a useful addition to both academic and industrial provider libraries..." (Measurement & Control, February 2003) "...fills the need for a comprehensive presentation of control theory at an elementary level..." (Zentralblatt Math, Vol.1002, No.02, 2003)Table of ContentsPreface. Process Monitoring versus Process Adjustment. Modeling Discrete-Time Dynamical Processes. ARIMA Time-Series Models. Transfer Function Modeling. Optimal Feedback Controllers. Discrete-Time PID Controllers. EWMA Feedback Controllers and the Run-to-Run Control Problem. Recursive Estimation and Adaptive Control. Analysis and Adjustment of Multivariate Processes. Data Files and Spreadsheets Used in the Book. Bibliography. Index.

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Book SynopsisWritten for people of various professions and offering a modern approach to using value analysis for product development, this is a structured process that unites interdisciplinary teams in an organization to select and analyze projects in terms of investment potential and to integrate quality and productivity. It contains four sections that describe the nature, measurement, design and management of value.Table of ContentsTHE NATURE OF VALUE. The Value Force. Value, Growth, and Evolution. THE MEASUREMENT OF VALUE. Value Measurement. Value Measurement Techniques. Modeling the Dynamics of Value. Value and Decision Making. THE DESIGN OF VALUE. Function Analysis. Quality Function Deployment: The Total Product Concept. The Technology Road Map. Customer-Oriented Product Concepting. THE MANAGEMENT OF VALUE. Valuism. Value Management Methodologies. Value Management: Behavioral and Organizational Aspects. Value Planning. Valuism, Value Management, and the Future. Index.

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Book SynopsisAs the Lead Reliability Engineer for Ford Motor Company, Guangbin Yang is involved with all aspects of the design and production of complex automotive systems.Trade Review"This book is quite different from traditional books written on reliability engineering so far and is authored by a person who has a rich industrial experience of working with Ford Motor Company. The book is quite informative and provides a good insight of methodologies and techniques used in reliability engineering. This will go a long way in creating competitive products that perform well in the market and also provide customer satisfaction." (International Journal of Performability Engineering; 1/09) "It is a very practical book which provides a comprehensive discussion on reliability engineering concepts and techniques throughout a product life cycle. The author has done a great job in explaining the up-to-date reliability techniques in a very practical manner and using simple and straightforward language. This book will prove very useful for reliability engineers, testing engineers, quality engineers and design engineers." (Reliability Review, December 2008) "This book gives both starting and experienced engineers a very nice overview of the different methods and tools that can be used for reliability engineering. It is very nice that the book gives a lot of (often simplified) examples; it will therefore not be difficult to apply the theory in industrial practice." (Quality and Reliability Engineering International, 2008) " This is a useful and an important book. It should be on the shelf of all reliability engineers and other engineers who have responsibility for product reliability. It will also be of interest to many of those doing research in the area. Overall, the book is well-written and easy to read." (Journal of Quality Technology, April 2008) "The author has done a great job in explaining the practical and state-of-the-art techniques to access and enhance reliability throughout the product life cycle. This book deliberates on a wide range of topics in reliability engineering. Practical examples and exercises, mostly from the automotive industry, are used to illustrate the ideas and methodologies. Readers of this book are expected to have some knowledge of basic statistical inference, statistical modeling, and probability theory. This book will be of practical use for a variety of engineers, including reliability engineers, quality engineers, test engineers, systems engineers, or design engineers, who are working in different stages of the product life cycle. It will also serve well as a textbook or a reference book to students in a course on reliability, quality, or industrial engineering." (Technometrics, February 2008)Table of Contents1 Reliability Engineering and Product Life Cycle 1 1.1 Reliability Engineering 1 1.2 Product Life Cycle 2 1.3 Integration of Reliability Engineering into the Product Life Cycle 5 1.4 Reliability in the Concurrent Product Realization Process 6 Problems 7 2 Reliability Definition Metrics and Product Life Distributions 9 2.1 Introduction 9 2.2 Reliability Definition 10 2.3 Reliability Metrics 12 2.4 Exponential Distribution 17 2.5 Weibull Distribution 19 2.6 Mixed Weibull Distribution 22 2.7 Smallest Extreme Value Distribution 24 2.8 Normal Distribution 26 2.9 Lognormal Distribution 28 Problems 31 3 Reliability Planning and Specification 33 3.1 Introduction 33 3.2 Customer Expectations and Satisfaction 34 3.3 Reliability Requirements 41 3.4 Reliability Program Development 48 3.5 Reliability Design and Design for Six Sigma 61 Problems 64 4 System Reliability Evaluation and Allocation 65 4.1 Introduction 65 4.2 Reliability Block Diagram 66 4.3 Series Systems 68 4.4 Parallel Systems 71 4.5 Mixed Configurations 73 4.6 k-out-of-n Systems 77 4.7 Redundant Systems 79 4.8 Reliability Evaluation of Complex Systems 84 4.9 Confidence Intervals for System Reliability 91 4.10 Measures of Component Importance 99 4.11 Reliability Allocation 106 Problems 118 5 Reliability Improvement Through Robust Design 122 5.1 Introduction 122 5.2 Reliability and Robustness 123 5.3 Reliability Degradation and Quality Loss 125 5.4 Robust Design Process 129 5.5 Boundary Definition and Interaction Analysis 132 5.6 P-Diagram 133 5.7 Noise Effects Management 134 5.8 Design of Experiments 136 5.9 Experimental Life Data Analysis 148 5.10 Experimental Degradation Data Analysis 152 5.11 Design Optimization 156 5.12 Robust Reliability Design of Diagnostic Systems 172 5.13 Case Study 179 5.14 Advanced Topics in Robust Design 181 Problems 190 6 Potential Failure Mode Avoidance 194 6.1 Introduction 194 6.2 Failure Mode and Effects Analysis 195 6.3 Advanced Topics in FMEA 208 6.4 Fault Tree Analysis 212 6.5 Advanced Topics in FTA 225 6.6 Computer-Aided Design Controls 230 Problems 235 7 Accelerated Life Tests 237 7.1 Introduction 237 7.2 Development of Test Plans 238 7.3 Common Stresses and Their Effects 246 7.4 Life–Stress Relationships 252 7.5 Graphical Reliability Estimation at Individual Test Conditions 266 7.6 Analytical Reliability Estimation at Individual Test Conditions 274 7.7 Reliability Estimation at Use Condition 292 7.8 Compromise Test Plans 302 7.9 Highly Accelerated Life Tests 326 Problems 327 8 Degradation Testing and Analysis 332 8.1 Introduction 332 8.2 Determination of the Critical Performance Characteristic 333 8.3 Reliability Estimation from Pseudo life 334 8.4 Degradation Analysis with Random-Effect Models 337 8.5 Degradation Analysis for Destructive Inspections 345 8.6 Stress-Accelerated Degradation Tests 351 8.7 Accelerated Degradation Tests with Tightened Thresholds 358 8.8 Accelerated Degradation Test Planning 364 Problems 373 9 Reliability Verification Testing 379 9.1 Introduction 379 9.2 Planning Reliability Verification Tests 380 9.3 Bogey Testing 383 9.4 Sample Size Reduction by Tail Testing 389 9.5 Sequential Life Testing 394 9.6 Reliability Verification Using Prior Information 406 9.7 Reliability Verification Through Degradation Testing 408 Problems 410 10 Stress Screening 412 10.1 Introduction 412 10.2 Screening Techniques 413 10.3 Design of Screen Plans 415 10.4 Principle of Degradation Screening 417 10.5 Part-Level Degradation Screening 419 10.6 Module-Level Screening 425 10.7 Module Reliability Modeling 431 10.8 Cost Modeling 433 10.9 Optimal Screen Plans 435 Problems 438 11 Warranty Analysis 442 11.1 Introduction 442 11.2 Warranty Policies 443 11.3 Warranty Data Mining 447 11.4 Reliability Estimation from Warranty Claim Times 451 11.5 Two-Dimensional Reliability Estimation 454 11.6 Warranty Repair Modeling 470 11.7 Warranty Cost Estimation 473 11.8 Field Failure Monitoring 477 11.9 Warranty Cost Reduction 480 Problems 482 Appendix: Orthogonal Arrays Linear Graphs and Interaction Tables 486 References 495 Index 511

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Book SynopsisThe comprehensive reference on modern techniques and methods for monitoring and inspecting corrosion Strategic corrosion inspection and monitoring can improve asset management and life cycle assessment and optimize operational budgets.Table of ContentsPreface. Chapter 1. Corrosion and its Cost in a Modern World. Chapter 2. Corrosion Detectability. Chapter 3. Maintenance, Management, and Inspection Strategies. Chapter 4. Corrosion Monitoring. Chapter 5. Nondestructive Evaluation. Appendix A. SI Units Conversion Table. Appendix B. Index.

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Book SynopsisBusiness Process Reengineering BreakPoint Strategies for MarketDominance Business Process Reengineering shows you how to take thevital next step to attain market dominance and become a worldleader * A team of internationally recognized Coopers & Lybrandmanufacturing consultants explain why they believe the businessworld needs to move beyond continuous improvement and TQM conceptsto Business Process Reengineering (BPR). * BPR involves a dramatic redesign of business processes,organization structures and use of technology, to achievebreakthroughs in business competitiveness. * The book is based on the authors experience of extensiveinternational work with leading corporations including AT&T,Asea Brown Boveri (ABB), Allied-Signal, and Coca-Cola &Schweppes (CC&SB). * Focusing on the effectiveness of BPR, the book shows howcompanies can streamline operations, and inevitably cut costs, onthe way to creating process excellence in all key aspects of theorganizatiTable of ContentsWhat Is the New Thinking?. Why the New Thinking?. Understanding Processes. Putting the China Back Together. Searching for BreakPoints. The New Assets. Process Management in Large Businesses. Organizing and Managing for Success. A Final Word. Appendix. Index.

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Book SynopsisCovering the complete range of data highways employed for measurement and control, this text aims to provide control engineers and product engineers with insights into a variety of market sectors. The author maintains a balance between theoretical discussion and practical application.Table of ContentsCommunication Media and Codes. Standards. Factory Automation and Process Control. Laboratory and Medical Automation. Intelligent Buildings. Transport. Electronic Systems. The Connected Future. Appendices. References. Index.

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Book SynopsisAs we move closer to a genuinely global economy, the pressure to develop highly reliable products on ever-tighter schedules will increase. Part of a designer''s toolbox for achieving product reliability in a compressed time frame should be a set of best practices for utilizing accelerated stress testing (AST). The Accelerated Stress Testing Handbook delineates a core set of AST practices as part of an overall methodology for enhancing hardware product reliability. The techniques presented will teach readers to identify design deficiencies and problems with component quality or manufacturing processes early in the product''s life, and then to take corrective action as quickly as possible. A wide array of case studies gleaned from leading practitioners of AST supplement the theory and methodology, which will provide the reader with a more concrete idea of how AST truly enhances quality in a reduced time frame. Important topics covered include: Theoretical bTrade Review"This is a most thorough and up-to-date handbook...I highly recommend it to all readers in this field of interest..." (IEEE Instrumentation and Measurement Magazine, December 2001)Table of ContentsFoerword (F. Ianna). Preface. Acknowledgments. OVERVIEW. Introduction (H. Chan and P. Englert). Principles of Stress Testing (H. Chan and P. Englert). PROCESS AND GUIDELINES. Stress Testing Program: Generic Processes (H. Chan and P. Englert). Stress Testing Program Subprocesses (H. Chan and P. Englert). Guidelines for Design and Manufacturing Stress Testing (H. Chan and P. Englert). THEORY. Economic and Optimization (H. Chan and P. Englert). Reliability Growth (C. Seusy). Overview of the Failure Analysis Process for Electrical Components (G. Pfeiffer). EQUIPMENT AND TECHNIQUES. Accelerated Stress Testing Equipment and Techniques (C. Felkins). Vibration and Shock Inputs Identify Some Failure Modes (W. Tustin). Relative Effectiveness of Thermal Cycling Versus Burn-In (K. Lo and F. LoVasco). Accelerated Qualification of Electronic Assemblies Under Combined Temperature Cycling and Vibration Environments: Is Miner's Hypothesis Valid (K. Upadhyayula and A. Dasgupta)? Liquid Environmental Stress Testing (LEST) (P. Englert). Safety Qualification of Stress Testing (S. Rajaram). BEST PRACTICES CASE STUDIES IN COMPUTER, COMMUNICATIONS, AND OTHER INDUSTRIES. Production Ast with Computers Using the Taguchi Method (D. Pachuki). Design Ast with Vendor Electronics (C. Schinner). Design and Production Ast with Power Supplies (D. Dalland). Design and Production Ast with Computers (E. Kyser). Qualifications and Production Sampling Ast with Printed Circuit Boards (H. McLean). Manufacturing Ast with Telecommunication Products (T. Parker and G. Harrison). Productionn Ast with Computer Disks. Benchmarking (H. Malec). Glossary of Stress Testing Terminology. Bibliography. Index. Epilogue. About the Editors.

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Book SynopsisExamines the use of different technologies to enhance community and asset resilience and provides a comprehensive set of practices after presenting and discussing the basis for those practices.

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Book SynopsisAn introduction to the subject of contamination control, with specific applications to the aerospace industry. The text examines methods to quantify the cleanliness level required by various contamination-sensitive surfaces and to predict the end-of-life contamination level for those surfaces.

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Book SynopsisIf your goal is 100% zero defects, here is the book for you — a completely illustrated guide to poka-yoke (mistake-proofing) for supervisors and shop-floor workers. Many poka-yoke ideas come from line workers and are implemented with the help of engineering staff or tooling or machine specialists. The result is better product quality and greater participation by workers in efforts to improve your processes, your products, and your company as a whole.The first section of the book uses a simple, illustrated format to summarize many of the concepts and main features of poka-yoke. The second section shows 240 examples of poka-yoke improvements implemented in Japanese plants.The book: Organizes examples according to the broad issue or problem they address. Pinpoints how poka-yoke applies to specific devices, parts and products, categories of improvement methods, and processes. Provides sample improvement forms for you to sketch out your own ideas. Table of ContentsPublisher's ForewordPrefaceIntroductionOverview of Poka-yoke240 Poka-Yoke ExamplesProcessing ErrorsAssembly ErrorsMounting ErrorsInclusion of Wrong ItemsInsertion ErrorsAssembly OmissionsProcessing OmissionsMeasurement ErrorsDimensional ErrorsOmissions of OperationsOperations ErrorsPasting/Labeling ErrorsInspection ErrorsWiring/Contact ErrorsPainting ErrorsPrinting ErrorsMisalignment Set-Up ErrorsPacking/Wrapping ErrorsMismatched Jigs and DiesRinsing ErrorsMiscellaneous ProblemsImprovement FormIndexes

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Book SynopsisSTATISTICAL QUALITY CONTROL Provides a basic understanding of statistical quality control (SQC) and demonstrates how to apply the techniques of SQC to improve the quality of products in various sectorsThis book introduces Statistical Quality Control and the elements of Six Sigma Methodology, illustrating the widespread applications that both have for a multitude of areas, including manufacturing, finance, transportation, and more. It places emphasis on both the theory and application of various SQC techniques and offers a large number of examples using data encountered in real life situations to support each theoretical concept. Statistical Quality Control: Using MINITAB, R, JMP and Python begins with a brief discussion of the different types of data encountered in various fields of statistical applications and introduces graphical and numerical tools needed to conduct preliminary analysis of the data. It then discusses the basic concept of statistical quality control (SQC) and Six Sigma Methodology and examines the different types of sampling methods encountered when sampling schemes are used to study certain populations. The book also covers Phase 1 Control Charts for variables and attributes; Phase II Control Charts to detect small shifts; the various types of Process Capability Indices (CPI); certain aspects of Measurement System Analysis (MSA); various aspects of PRE-control; and more. This helpful guide alsoFocuses on the learning and understanding of statistical quality control for second and third year undergraduates and practitioners in the fieldDiscusses aspects of Six Sigma MethodologyTeaches readers to use MINITAB, R, JMP and Python to create and analyze chartsRequires no previous knowledge of statistical theoryIs supplemented by an instructor-only book companion site featuring data sets and a solutions manual to all problems, as well as a student book companion site that includes data sets and a solutions manual to all odd-numbered problemsStatistical Quality Control: Using MINITAB, R, JMP and Python is an excellent book for students studying engineering, statistics, management studies, and other related fields and who are interested in learning various techniques of statistical quality control. It also serves as a desk reference for practitioners who work to improve quality in various sectors, such as manufacturing, service, transportation, medical, oil, and financial institutions. Its also useful for those who use Six Sigma techniques to improve the quality of products in such areas.Table of ContentsPreface xiii About the Companion Website xix 1 Quality Improvement and Management 1 1.1 Introduction 1 1.2 Statistical Quality Control 1 1.2.1 Quality and the Customer 2 1.2.2 Quality Improvement 3 1.2.3 Quality and Productivity 5 1.3 Implementing Quality Improvement 6 1.3.1 Outcomes of Quality Control 7 1.3.2 Quality Control and Quality Improvement 7 1.3.2.1 Acceptance Sampling Plans 7 1.3.2.2 Process Control 8 1.3.2.3 Removing Obstacles to Quality 8 1.3.2.4 Eliminating Productivity Quotas 9 1.3.3 Implementing Quality Improvement 9 1.4 Managing Quality Improvement 10 1.4.1 Management and Their Responsibilities 10 1.4.2 Management and Quality 11 1.4.3 Risks Associated with Making Bad Decisions 11 1.5 Conclusion 12 2 Basic Concepts of the Six Sigma Methodology 13 2.1 Introduction 13 2.2 What Is Six Sigma? 13 2.2.1 Six Sigma as a Management Philosophy 13 2.2.2 Six Sigma as a Systemic Approach to Problem Solving 14 2.2.3 Six Sigma as a Statistical Standard of Quality 15 2.2.3.1 Statistical Basis for Six Sigma 16 2.2.4 Six Sigma Roles 18 2.3 Is Six Sigma New? 19 2.4 Quality Tools Used in Six Sigma 20 2.4.1 The Basic Seven Tools and the New Seven Tools 20 2.4.2 Lean Tools 21 2.4.2.1 Eight Wastes 21 2.4.2.2 Visual Management 22 2.4.2.3 The 5S Method 23 2.4.2.4 Value-Stream Mapping 24 2.4.2.5 Mistake-Proofing 24 2.4.2.6 Quick Changeover 24 2.5 Six Sigma Benefits and Criticism 25 2.5.1 Why Do Some Six Sigma Initiatives Fail? 25 Review Practice Problems 26 3 Describing Quantitative and Qualitative Data 27 3.1 Introduction 27 3.2 Classification of Various Types of Data 27 3.3 Analyzing Data Using Graphical Tools 29 3.3.1 Frequency Distribution Tables for Qualitative and Quantitative Data 29 3.3.1.1 Qualitative Data 30 3.3.1.2 Quantitative Data 31 3.4 Describing Data Graphically 33 3.4.1 Dot Plots 33 3.4.2 Pie Charts 34 3.4.3 Bar Charts 36 3.4.4 Histograms 41 3.4.5 Line Graphs 43 3.4.6 Measures of Association 45 3.5 Analyzing Data Using Numerical Tools 49 3.5.1 Numerical Measures 49 3.5.2 Measures of Centrality 49 3.5.2.1 Mean 50 3.5.2.2 Median 51 3.5.2.3 Mode 53 3.5.3 Measures of Dispersion 54 3.5.3.1 Range 54 3.5.3.2 Variance 55 3.5.3.3 Standard Deviation 56 3.5.3.4 Empirical Rule 58 3.5.3.5 Interquartile Range 60 3.5.4 Box-and-Whisker Plot 61 3.6 Some Important Probability Distributions 63 3.6.1 The Binomial Distribution 63 3.6.1.1 Binomial Probability Tables 64 3.6.2 The Hypergeometric Distribution 65 3.6.2.1 Mean and Standard Deviation of a Hypergeometric Distribution 68 3.6.3 The Poisson Distribution 69 3.6.3.1 Mean and Standard Deviation of a Poisson Distribution 71 3.6.3.2 Poisson Probability Tables 71 3.6.4 The Normal Distribution 73 Review Practice Problems 80 4 Sampling Methods 89 4.1 Introduction 89 4.2 Basic Concepts of Sampling 89 4.2.1 Introducing Various Sampling Designs 91 4.3 Simple Random Sampling 93 4.3.1 Estimating the Population Mean and Population Total 94 4.3.2 Confidence Interval for the Population Mean and Population Total 98 4.3.3 Determining Sample Size 99 4.4 Stratified Random Sampling 100 4.4.1 Estimating the Population Mean and Population Total 100 4.4.2 Confidence Interval for the Population Mean and Population Total 103 4.4.3 Determining Sample Size 105 4.5 Systematic Random Sampling 107 4.5.1 Estimating the Population Mean and Population Total 107 4.5.2 Confidence Interval for the Population Mean and Population Total 109 4.5.3 Determining the Sample Size 109 4.6 Cluster Random Sampling 112 4.6.1 Estimating the Population Mean and Population Total 112 4.6.2 Confidence Interval for the Population Mean and Population Total 114 4.6.3 Determining the Sample Size 116 Review Practice Problems 117 5 Phase I Quality Control Charts for Variables 123 5.1 Introduction 123 5.2 Basic Definition of Quality and Its Benefits 123 5.3 Statistical Process Control 124 5.3.1 Check Sheets 126 5.3.2 Pareto Chart 128 5.3.3 Cause-and-Effect (Fishbone or Ishikawa) Diagrams 129 5.3.4 Defect-Concentration Diagrams 130 5.3.5 Run Charts 131 5.4 Control Charts for Variables 133 5.4.1 Process Evaluation 133 5.4.2 Action on the Process 133 5.4.3 Action on the Output 133 5.4.4 Variation 133 5.4.4.1 Common Causes (Random Causes) 134 5.4.4.2 Special Causes (Assignable Causes) 134 5.4.4.3 Local Actions and Actions on the System 134 5.4.4.4 Relationship Between Types of Variation 134 5.4.5 Control Charts 135 5.4.5.1 Preparation for Using Control Charts 136 5.4.5.2 Benefits of Control Charts 137 5.4.5.3 Rational Samples for control Charts 137 5.5 Shewhart X and R Control Charts 141 5.5.1 Calculating Sample Statistics 141 5.5.2 Calculating Control Limits 141 5.5.3 Interpreting Shewhart X and R Control Charts 146 5.5.4 Extending the Current Control Limits for Future Control 147 5.6 Shewhart X and R Control Charts When the Process Mean and Standard Deviation are Known 149 5.7 Shewhart X and R Control Charts for Individual Observations 150 5.8 Shewhart X and S Control Charts with Equal Sample Sizes 154 5.9 Shewhart X and S Control Charts with Variable Sample Sizes 157 5.10 Process Capability 161 6 Phase I Control Charts for Attributes 173 6.1 Introduction 173 6.2 Control Charts for Attributes 173 6.3 The p Chart: Control Charts for Nonconforming Fractions with Constant Sample Sizes 174 6.3.1 Control Limits for the p Control Chart 175 6.3.2 Interpreting the Control Chart for Nonconforming Fractions 176 6.4 The p Chart: Control Chart for Nonconforming Fractions with Variable Samples Sizes 179 6.5 The np Chart: Control Charts for the Number of Nonconforming Units 182 6.5.1 Control Limits for np Control Charts 183 6.6 The c Control Chart – Control Charts for Nonconformities per Sample 184 6.7 The u Chart 188 Review Practice Problems 193 7 Phase II Quality Control Charts for Detecting Small Shifts 201 7.1 Introduction 201 7.2 Basic Concepts of CUSUM Control Charts 202 7.2.1 CUSUM Control Charts vs. Shewhart X and R Control Charts 202 7.3 Designing a CUSUM Control Chart 205 7.3.1 Two-Sided CUSUM Control Charts Using the Numerical Procedure 206 7.3.2 The Fast Initial Response (FIR) Feature for CUSUM Control Charts 212 7.3.3 One-Sided CUSUM Control Charts 216 7.3.4 Combined Shewhart-CUSUM Control Charts 216 7.3.5 CUSUM Control Charts for Controlling Process Variability 217 7.4 Moving Average (MA) Control Charts 218 7.5 Exponentially Weighted Moving Average (EWMA) Control Charts 223 Review Practice Problems 229 8 Process and Measurement System Capability Analysis 237 8.1 Introduction 237 8.2 Development of Process Capability Indices 238 8.3 Various Process Capability Indices 239 8.3.1 Process Capability Index: Cp 239 8.3.2 Process Capability Index: Cpk 244 8.3.3 Process Capability Index: Cpm 247 8.3.4 Process Capability Index: Cpmk 249 8.3.5 Process Capability Index: Cpnst 250 8.3.5.1 Comparing Cpnst with Cpk and Cpm 252 8.3.5.2 Other Features of Cpnst 253 8.3.6 Process Performance Indices: Pp and Ppk 254 8.4 Pre-control 255 8.4.1 Global Perspective on the Use of Pre-control – Understanding the Color-Coding Scheme 257 8.4.2 The Mechanics of Pre-control 258 8.4.3 The Statistical Basis for Pre-control 259 8.4.4 Advantages and Disadvantages of Pre-control 259 8.4.4.1 Advantages of Pre-control 260 8.4.4.2 Disadvantages of Pre-control 260 8.5 Measurement System Capability Analysis 261 8.5.1 Evaluating Measurement System Performance 262 8.5.2 The Range Method 262 8.5.3 The ANOVA Method 268 8.5.4 Graphical Representation of a Gauge R&R Study 272 8.5.5 Another Measurement Capability Index 275 Review Practice Problems 275 9 Acceptance Sampling Plans 283 9.1 Introduction 283 9.2 The Intent of Acceptance Sampling Plans 283 9.3 Sampling Inspection vs. 100% Inspection 284 9.4 Classification of Sampling Plans 284 9.4.1 Formation of Lots for Acceptance Sampling Plans 285 9.4.2 The Operating Characteristic (OC) Curve 285 9.4.3 Two Types of OC Curves 288 9.4.4 Some Terminology Used in Sampling Plans 288 9.5 Acceptance Sampling by Attributes 292 9.5.1 Acceptable Quality Limit (AQL) 292 9.5.2 Average Outgoing Quality (AOQ) 292 9.5.3 Average Outgoing Quality Limit (AOQL) 293 9.5.4 Average Total Inspection (ATI) 293 9.6 Single Sampling Plans for Attributes 296 9.7 Other Types of Sampling Plans for Attributes 296 9.7.1 Double-Sampling Plans for Attributes 296 9.7.2 The OC Curve 297 9.7.3 Multiple-Sampling Plans 301 9.7.4 Average Sample Number 301 9.7.5 Sequential-Sampling Plans 301 9.8 ANSI/ASQ Z1.4-2003 Sampling Standard and Plans 305 9.8.1 Levels of Inspection 306 9.8.2 Types of Sampling 308 9.9 Dodge-Romig Tables 309 9.10 ANSI/ASQ Z1.9-2003 Acceptance Sampling Plans by Variables 309 9.10.1 ANSI/ASQ Z1.9-2003 – Variability Known 310 9.10.2 Variability Unknown – Standard Deviation Method 311 9.10.3 Variability Unknown – Range Method 313 9.11 Continuous-Sampling Plans 314 9.11.1 Types of Continuous-Sampling Plans 315 9.11.2 Dodge’s Continuous Sampling Plans 315 9.11.3 MIL-STD-1235B 315 Review Practice Problems 316 10 Computer Resources to Support SQC: Minitab, R, JMP, and Python 339 Appendix A Statistical Tables 341 Appendix B Answers to Selected Practice Problems 357 Bibliography 361 Index 365

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Book SynopsisModern Industrial Statistics The new edition of the prime reference on the tools of statistics used in industry and services, integrating theoretical, practical, and computer-based approachesModern Industrial Statistics is a leading reference and guide to the statistics tools widely used in industry and services. Designed to help professionals and students easily access relevant theoretical and practical information in a single volume, this standard resource employs a computer-intensive approach to industrial statistics and provides numerous examples and procedures in the popular R language and for MINITAB and JMP statistical analysis software. Divided into two parts, the text covers the principles of statistical thinking and analysis, bootstrapping, predictive analytics, Bayesian inference, time series analysis, acceptance sampling, statistical process control, design and analysis of experiments, simulation and computer experiments, and reliability and survival anaTable of ContentsPreface to the third edition Preface to the second edition (abbreviated) Preface to the first edition (abbreviated) List of abbreviations Part A: Modern Statistics: A Computer Based Approach 1 Statistics and Analytics in Modern Industry 2 Analyzing Variability: Descriptive Statistics 3 Probability Models and Distribution Functions 4 Statistical Inference and Bootstrapping 5 Variability in Several Dimensions and Regression Models 6 Sampling for Estimation of Finite Population Quantities 7. Time Series Analysis and Prediction 8 Modern analytic methods Part B: Modern Industrial Statistics: Design and Control of Quality and Reliability 9 The Role of Industrial Analytics in Modern Industry 10 Basic Tools and Principles of Process Control 11 Advanced Methods of Statistical Process Control 12 Multivariate Statistical Process Control 13 Classical Design and Analysis of Experiments 14 Quality by Design 15 Computer Experiments 16 Reliability Analysis 17 Bayesian Reliability Estimation and Prediction 18 Sampling Plans for Batch and Sequential Inspection List of R packages References Author index Subject index Solution manual Appendices (available on book�s website) Appendix I Intro to R Appendix II Intro to MINITAB and Matrix Algebra Appendix III R scripts Appendix IV mistat Appendix V csv Files Appendix VI MINITAB macros Appendix VII JMP scripts

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Book SynopsisINTRODUCTION TO LOGISTICS SYSTEMS MANAGEMENT The updated new edition of the award-winning introductory textbook on logistics system management Introduction to Logistics Systems Management provides an in-depth introduction to the methodological aspects of planning, organization, and control of logistics for organizations in the private, public and non-profit sectors. Based on the authors' extensive teaching, research, and industrial consulting experience, this classic textbook is used in universities worldwide to teach students the use of quantitative methods for solving complex logistics problems. Fully updated and revised, the third edition places increased emphasis on the complexity and flexibility required by modern logistics systems. In this context, the extensive use of data, descriptive analytics, predictive models, and optimization techniques will be invaluable to support the decisions and actions of logistics and supply chain managers. Throughout the book, brand-new case studies and numerical examples illustrate how various methods can be used in industrial and service logistics to reduce costs and improve service levels. The book: includes new models and techniques that have emerged over the past decade; describes methodologies for logistics decision making, forecasting, logistics system design, procurement, warehouse management, and freight transportation management; includes end-of-chapter exercises, Microsoft Excel files and Python computer codes for each algorithm covered; includes access to a companion website with additional exercises, links to video tutorials, and supplementary teaching material. To facilitate creation of course material, additional LaTeX source data containing the formulae, optimization models, tables and algorithms described in the book is available to instructors. Introduction to Logistics Systems Management, Third Edition remains an essential textbook for senior undergraduate and graduate students in engineering, computer science, and management science courses. It is also a highly useful reference for academic researchers and industry practitioners alike.Table of ContentsForeword xiii Preface xv Acknowledgements xvii About the Authors xviii List of Abbreviations xix 1 Introducing Logistics 1 1.1 Definition of Logistics 1 1.2 Logistics Systems 3 1.3 Supply Chains 5 1.3.1 Logistics Versus Supply Chain Management 5 1.3.2 A Taxonomy of Supply Chains 5 1.3.3 The Bullwhip Effect 6 1.4 Logistics Service Providers 8 1.5 Logistics in Service Organizations 9 1.5.1 Logistics in Solid Waste Management 9 1.5.2 Humanitarian Logistics 10 1.6 Case Studies 11 1.6.1 Apple 11 1.6.2 Adidas AG 13 1.6.3 Galbani 14 1.6.4 Pfizer 15 1.6.5 Amazon 18 1.6.6 FedEx 20 1.6.7 A.P. Moller-Maersk 21 1.6.8 Canadian Pacific Railway 23 1.7 Trends in Logistics 24 1.7.1 Reverse and Sustainable Logistics 24 1.7.2 E-commerce Logistics 26 1.7.3 City Logistics 28 1.8 Logistics Objectives and KPIs 30 1.8.1 Capital-related KPIs 30 1.8.2 Cost-related KPIs 31 1.8.3 Service Level-related KPIs 32 1.9 Logistics Management 36 1.9.1 Logistics Planning 37 1.9.2 Logistics Organizational Structures 37 1.9.3 Controlling 41 1.10 Data Analytics in Logistics 48 1.10.1 Descriptive Analytics 48 1.10.2 Predictive Analytics 49 1.10.3 Prescriptive Analytics 49 1.11 Segmentation Analysis 69 1.11.1 Customer Segmentation 69 1.11.2 Product Segmentation 70 1.12 Information Systems 73 1.13 Questions and Problems 75 2 Forecasting Logistics Data 83 2.1 Introduction 83 2.2 Qualitative Methods 84 2.3 Quantitative Methods 85 2.3.1 Explanatory Versus Extrapolation Methods 87 2.3.2 The Forecasting Process 87 2.4 Exploratory Data Analysis 88 2.4.1 The Univariate Case 88 2.4.2 Histograms 89 2.4.3 Boxplots 90 2.4.4 Time Series Plots 92 2.4.5 The Bivariate Case 92 2.4.6 Scatterplots 93 2.5 Data Preprocessing 93 2.5.1 Insertion of Missing Data 93 2.5.2 Detection of Outliers 95 2.5.3 Data Aggregation 96 2.5.4 Removing Calendar Variations 98 2.5.5 Deflating Monetary Time Series 99 2.5.6 Adjusting for Population Variations 101 2.5.7 Data Normalization 101 2.6 Classification of Time Series 102 2.7 Explanatory Methods 105 2.7.1 Forecasting with Regression 105 2.7.2 Multicollinearity 107 2.7.3 Categorical Predictors 107 2.7.4 Coefficient of Determination 108 2.7.5 Polynomial Regression 109 2.7.6 Linear–log, Log–linear and Log–log Regression Models 111 2.7.7 Underfitting and Overfitting 111 2.7.8 Forecasting with Machine Learning 113 2.8 Extrapolation Methods 118 2.8.1 Notation 118 2.8.2 Decomposition Method 119 2.8.3 Further Extrapolation Methods: the Constant-trend Case 127 2.8.4 Further Extrapolation Methods: the Linear-trend Case 132 2.8.5 Further Extrapolation Methods: the Seasonality Case 137 2.8.6 Further Extrapolation Methods: the Irregular Time Series Case 146 2.8.7 Further Extrapolation Methods: the Intermittent Time Series Case 148 2.9 Accuracy Measures 154 2.9.1 Calibration of the Parametrized Forecasting Methods 155 2.9.2 Selection of the Most Accurate Forecasting Method 157 2.10 Forecasting Control 158 2.10.1 Tracking Signal 158 2.10.2 Control Charts 159 2.11 Interval Forecasts 162 2.12 Case Study: Sales Forecasting at Shivoham 163 2.13 Case Study: Sales Forecasting at Orlea 164 2.14 Questions and Problems 165 3 Designing the Logistics Network 177 3.1 Introduction 177 3.2 Classification of Logistics Network Design Problems 178 3.3 The Number of Facilities in a Logistics System 181 3.4 Qualitative Versus Quantitative Location Methods 183 3.5 The Weighted Scoring Method 183 3.6 The Analytical Hierarchy Process 185 3.7 Single-commodity One-echelon Continuous Location Problems 190 3.8 Single-commodity Two-echelon Continuous Location Problems 197 3.9 Single-commodity One-echelon Discrete Location Problems 200 3.10 Single-commodity Two-echelon Discrete Location Problems 222 3.11 The Multi-commodity Case 226 3.12 Location-covering Problems 230 3.13 p-centre Problems 234 3.14 Data Aggregation 241 3.15 Location Models Under Uncertainty 244 3.15.1 A Stochastic Location–allocation Model 244 3.15.2 A Location-routing Model with Uncertain Demand 247 3.16 Case Study: Intermodal Container Depot Location at Hardcastle 251 3.17 Case Study: Location–Allocation Decisions at the Italian National Transplant Centre 254 3.18 Questions and Problems 256 4 Selecting the Suppliers 267 4.1 Introduction 267 4.2 Definition of the Set of Potential Suppliers 269 4.3 Definition of the Selection Criteria 270 4.4 Supplier Selection 274 4.5 Supplier Relationship Management Software 278 4.6 Case Study: the System for the Selection of Suppliers at Baxter 279 4.7 Case Study: the Supplier Selection at Onokar 282 4.8 Questions and Problems 284 5 Managing a Warehouse 290 5.1 Introduction 290 5.1.1 Warehouse Operations 290 5.1.2 Warehouse Functional Zones 292 5.1.3 Advantages of Warehousing 294 5.2 Types of Warehouses 294 5.2.1 Classification with Respect to the Position in the Logistics System 294 5.2.2 Classification with Respect to Ownership 296 5.2.3 Classification with Respect to Climate-control 297 5.2.4 Classification with Respect to the Level of Automation 297 5.3 Warehousing Costs 298 5.4 Unit Loads 300 5.4.1 Freight Classification 300 5.4.2 Unit Loads and Stock Keeping Units 301 5.4.3 Packaging 301 5.4.4 Palletized Unit Loads 302 5.4.5 Containerized Unit Loads 305 5.5 Storage Systems 307 5.5.1 Block Stacking 307 5.5.2 Pallet Racks 307 5.5.3 Shelves 311 5.5.4 Cabinet and Carousel Systems 313 5.6 Internal Transportation Systems 314 5.6.1 Manual Handling and Non-autonomous Vehicles 315 5.6.2 Automated Guided Vehicles 318 5.6.3 Stacker Cranes 320 5.6.4 Conveyors 321 5.7 Product Identification Systems 322 5.7.1 SKU Codes 322 5.7.2 Global Trade Item Numbers 323 5.7.3 Barcodes 323 5.7.4 QR Codes 325 5.7.5 Logistic Labels 325 5.7.6 Radio-frequency Identification 325 5.8 Warehouse Performance Measures 327 5.9 Warehouse Management Systems 333 5.10 Warehouse Design 335 5.10.1 Internal Transportation Technology Selection 336 5.10.2 Layout Design 337 5.10.3 Sizing of the Storage Zone 341 5.10.4 Sizing of the Receiving and Shipping Zones 348 5.10.5 Sizing of an AS/RS 349 5.10.6 Sizing a Vehicle-based Internal Transportation System 354 5.11 Storage Space Allocation 355 5.12 Inventory Management 360 5.12.1 Deterministic models 361 5.12.2 Stochastic Models 373 5.12.3 Selecting an Inventory Policy 380 5.12.4 Multiproduct Inventory Models 382 5.13 Crossdock Door Assignment Problem 387 5.14 Put-away and Order Picking Optimization 390 5.14.1 Parts-to-picker Systems 390 5.14.2 Picker-to-parts and AGV-based Systems 390 5.15 Load Consolidation 397 5.15.1 One-dimensional Bin Packing Problems 400 5.15.2 Two-dimensional Bin Packing Problems 403 5.15.3 Three-dimensional Bin Packing Problems 406 5.16 Case Study: Inventory Management at Wolferine 415 5.17 Case Study: Airplane Loading at FedEx 416 5.18 Questions and problems 418 6 Managing Freight Transportation 431 6.1 Introduction 431 6.2 Transportation Modes 431 6.2.1 Road Transportation 432 6.2.2 Water Transportation 434 6.2.3 Rail Transportation 437 6.2.4 Air Transportation 438 6.2.5 Pipeline Transportation 439 6.2.6 Intermodal Transportation 439 6.2.7 Comparison Among Transportation Modes 440 6.3 Freight Transportation Terminals 443 6.3.1 Port Terminals 444 6.3.2 Air Cargo Terminals 446 6.3.3 Rail Freight Terminals 448 6.3.4 Road Freight Terminals 449 6.4 Classification of Freight Transportation Management Problems 450 6.4.1 Long-haul Freight Transportation Management 450 6.4.2 Freight Transportation Terminal Management 451 6.4.3 Short-haul Freight Transportation Management 452 6.5 Transportation Management Systems 454 6.6 Freight Traffic Assignment Problems 455 6.6.1 Minimum-cost Flow Formulation 456 6.6.2 Linear Single-commodity Minimum-cost Flow Problems 458 6.6.3 Linear Multi-commodity Minimum-cost Flow Problems 465 6.7 Service Network Design Problems 471 6.8 Vehicle Allocation Problems 478 6.9 A Dynamic Driver Assignment Problem 481 6.10 Vehicle Fleet Composition 483 6.11 Shipment Consolidation 485 6.12 Vehicle Routing Problems 488 6.12.1 The Travelling Salesman Problem 491 6.12.2 The Node Routing Problem with Operational Constraints 506 6.12.3 The Node Routing and Scheduling Problem with Time Windows 519 6.12.4 Arc Routing Problems 530 6.12.5 Route Sequencing 540 6.13 Real-time Vehicle Routing Problems 541 6.14 Integrated Location and Routing Problems 543 6.15 Inventory Routing Problems 545 6.16 Case Study: Air Network Design at Intexpress 555 6.17 Case Study: Dynamic Vehicle-dispatching Problem with Pickups and Deliveries at eCourier 559 6.18 Questions and Problems 561 Index 572

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Book SynopsisTable of Contents 1. Introduction to Quality Improvement 2. Lean Enterprise 3. Six Sigma 4. Fundamentals of Statistics 5. Statistical Process Control 6. Control Charts for Variables 7. Additional SPC Techniques for Variables 8. Fundamentals of Probability 9. Control Charts for Attributes 10. Acceptance Sampling. 11. Reliability 12. Management and Planning Tools 13. Experimental Design 14. Taguchi’s Quality Engineering Appendix Table A. Areas under the Normal Curve Table B. Factors for Computing Central Lines and 3 Control Limits for S, and R Charts Table C. The Poisson Distribution Table D. Random Numbers Table E. Commonly Used Conversion Factors Table-F. Critical Values of T Distribution Table G-1. Critical Values of F Distribution (a = 0.1) Table G-2. Critical Values of F Distribution (a = 0.05) Table G-3. Critical Values of F Distribution (a = 0.01) Table H. Orthogonal Arrays Selected Bibliography Answers to Selected Exercises Index

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Book SynopsisWith 588 images and associated analyses for avoidance of damage to plastics, this manual is aimed at both professionals and students. Many technical terms and colloquial descriptions, explanations, and interconnections with related areas, together with the images, facilitate the reader in determining and describing the exact type of damage of a given sample. The images, from microscopic quality and damage analysis of molding materials, semi-finished products, and molded parts, are divided into 74 subject areas of plastics processing and application, and are classified based on over 2620 industry-standard technical terms. The analyses were performed with various light microscopes and a scanning electron microscope.

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Book SynopsisThe validation of equipment, processes and methods is a basic requirement that nowadays has to be met in most industries. This handbook deals with the validation of computerized systems in general as well as with analytical method validation. The many detailed practical examples focus on thermal analysis of materials, such as plastics and rubber.The handbook is intended for newcomers interested in the theoretical and regulatory aspects of validation and for thermal analysis practitioners who have to validate their equipment and methods.Table of Contents Part 1: Validation of Computerized Systems Recent Changes in Regulations and Regulatory Guidance Instrument Qualification, Computerized System Validation and Method Validation Regulatory Requirements for Computerized System Validation Computerized System Validation Writing the User Requirements Specification (URS) Auditing the System Supplier Installation Qualification and Operational Qualification (IQ and OQ) Performance Qualification (PQ) or End User Testing Part 2: Method Validation Measurement Errors and Uncertainty of Measurement Validation of Analytical Procedures and Methods Interlaboratory Studies in Thermal Analysis Method Development Through to SOP Practical Examples Appendix 1: 21 CFR Part 11 and EU GMP Annex 11 Appendix 2: Basic Statistics Appendix 3: Standard Test Methods for Thermal Analysis

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Book SynopsisProcess control is a multidisciplinary field that encompasses a combination of analytical and process chemistry, process engineering and multivariate data analysis. Process analytical techniques (PATs) involve monitoring and control of chemical and physical processes in order to obtain products with desired properties, to improve manufacturing efficiency and reduce process costs. PATs have been utilised in various industrial branches to gain a deeper understanding of particular process stages, identify crucial control parameters, ensure the product quality by optimal design and determine the process disturbances. This book discusses strategic management accounting and green supply chain management; analyses the plant-wide control structures for industrial processes''; the use of vibrational spectroscopy as a tool for in-line process monitoring; and effects of resolution of measurements in the behaviour of cumulative sum control charts.

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Book SynopsisManufacturing plays an important role in the nation''s economy, employment, and national defence. Accordingly, Congress has maintained a strong interest in the health of the U.S. manufacturing sector. The Obama Administration has undertaken a number of initiatives intended to support U.S. manufacturing, including establishment of the Advanced Manufacturing Partnership, Advanced Manufacturing National Program Office (AMNPO), Advanced Manufacturing Technology Consortia program, National Robotics Initiative, and Materials Genome Initiative. In his FY2013 budget, President Obama proposed the creation of a National Network for Manufacturing Innovation (NNMI) to help accelerate innovation by investing in industrially relevant manufacturing technologies with broad applications, and to support manufacturing technology commercialisation by bridging the gap between the laboratory and the market. This book describes the National Network for Manufacturing Innovation while providing guidance on intellectual property rights for it, as well as institute performance metrics for NNMI.

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Book SynopsisMonitoring and diagnosis of electrical machine faults is a scientific and economic issue which is motivated by objectives for reliability and serviceability in electrical drives. This book provides a survey of the techniques used to detect the faults occurring in electrical drives: electrical, thermal and mechanical faults of the electrical machine, faults of the static converter and faults of the energy storage unit. Diagnosis of faults occurring in electrical drives is an essential part of a global monitoring system used to improve reliability and serviceability. This diagnosis is performed with a large variety of techniques: parameter estimation, state observation, Kalman filtering, spectral analysis, neural networks, fuzzy logic, artificial intelligence, etc. Particular emphasis in this book is put on the modeling of the electrical machine in faulty situations. Electrical Machines Diagnosis presents original results obtained mainly by French researchers in different domains. It will be useful as a guideline for the conception of more robust electrical machines and indeed for engineers who have to monitor and maintain electrical drives. As the monitoring and diagnosis of electrical machines is still an open domain, this book will also be very useful to researchers.Table of ContentsPreface xi Chapter 1. Faults in Electrical Machines and their Diagnosis 1 Sadok BAZINE and Jean-Claude TRIGEASSOU 1.1. Introduction 1 1.2. Composition of induction machines 3 1.3. Failures in induction machines 5 1.4. Overview of methods for diagnosing induction machines 10 1.5. Conclusion 18 1.6. Bibliography 19 Chapter 2. Modeling Induction Machine Winding Faults for Diagnosis 23 Emmanuel SCHAEFFER and Smail BACHIR 2.1. Introduction 23 2.2. Study framework and general methodology 26 2.3. Model of the machine with a stator insulation fault 40 2.4. Generalization of the approach to the coupled modeling of stator and rotor faults 51 2.5. Methodology for monitoring the induction machine 57 2.6. Conclusion 64 2.7. Bibliography 67 Chapter 3. Closed-Loop Diagnosis of the Induction Machine 69 Imène BEN AMEUR BAZINE, Jean-Claude TRIGEASSOU, Khaled JELASSI and Thierry POINOT 3.1. Introduction 69 3.2. Closed-loop identification 71 3.3. General methodology of closed-loop identification of induction machine 74 3.4. Closed-loop diagnosis of simultaneous stator/rotor faults 82 3.5. Conclusion 89 3.6. Bibliography 90 Chapter 4. Induction Machine Diagnosis Using Observers 93 Guy CLERC and Jean-Claude MARQUES 4.1. Introduction 93 4.2. Model presentation 96 4.3. Observers 104 4.4. Applying observers to diagnostics 119 4.5. Conclusion 127 4.6. Bibliography 128 Chapter 5. Thermal Monitoring of the Induction Machine 131 Luc LORON and Emmanuel FOULON 5.1. Introduction 131 5.2. Real-time parametric estimation by Kalman filter 137 5.3. Electrical models for the thermal monitoring 142 5.4. Experimental system 149 5.5. Experimental results 157 5.6. Conclusion 162 5.7. Appendix: induction machine characteristics 163 5.8. Bibliography 163 Chapter 6. Diagnosis of the Internal Resistance of an Automotive Lead-acid Battery by the Implementation of a Model Invalidation-based Approach: Application to Crankability Estimation 167 Jocelyn SABATIER, Mikaël CUGNET, Stéphane LARUELLE, Sylvie GRUGEON, Isabelle CHANTEUR, Bernard SAHUT, Alain OUSTALOUP and Jean-Marie TARASCON 6.1. Introduction 167 6.2. Fractional model of a lead-acid battery for the start-up phase 169 6.3. Identification of the fractional model 171 6.4. Battery resistance as crankability estimator 175 6.5. Model validation and estimation of the battery resistance 178 6.6. Toward a battery state estimator 188 6.7. Conclusion 188 6.8. Bibliography 190 Chapter 7. Electrical and Mechanical Faults Diagnosis of Induction Machines using Signal Analysis 193 Hubert RAZIK and Mohamed EL KAMEL OUMAAMAR 7.1. Introduction 193 7.2. The spectrum of the current line 194 7.3. Signal processing 196 7.4. Signal analysis from experiment campaigns 199 7.5. Conclusion 222 7.6. Appendices 223 7.7. Bibliography 224 Chapter 8. Fault Diagnosis of the Induction Machine by Neural Networks 227 Monia Ben Khader BOUZID, Najiba MRABET BELLAAJ, Khaled JELASSI, Gérard CHAMPENOIS and Sandrine MOREAU 8.1. Introduction 227 8.2. Methodology of the use of the ANN in the diagnostic domain 228 8.3. Description of the monitoring system 232 8.4. The detection problem 233 8.5. The proposed method for the robust detection 235 8.6. Signature of the stator and rotor faults 237 8.7. Detection of the faults by the RNd neural network 244 8.8. Diagnosis of the stator fault 251 8.9. Diagnosis of the rotor fault 263 8.10. Complete monitoring system of the induction machine 267 8.11. Conclusion 268 8.12. Bibliography 269 Chapter 9. Faults Detection and Diagnosis in a Static Converter 271 Mohamed BENBOUZID, Claude DELPHA, Zoubir KHATIR, Stéphane LEFEBVRE and Demba DIALLO 9.1. Introduction 271 9.2. Detection and diagnosis 273 9.3. Thermal fatigue of power electronic moduli and failure modes 294 9.4. Conclusion 316 9.5. Bibliography 316 List of Authors 321 Index 327

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Book SynopsisThis book is a general presentation of complex systems, examined from the point of view of management. There is no standard formula to govern such systems, nor to effectively understand and respond to them. The interdisciplinary theory of self-organization is teeming with examples of living systems that can reorganize at a higher level of complexity when confronted with an external challenge of a certain magnitude. Modern businesses, considered as complex systems, ideally know how to flexibly and resiliently adapt to their environment, and also how to prepare for change via self-organization. Understanding sources of potential crisis is essential for leaders, though not all crises are necessarily bad news, as creative firms know how to respond to challenges through innovation: new products and markets, organizational learning for collective intelligence, and more.Table of ContentsPreface xi Part 1. Programmation 1 Chapter 1. Linear Programming 3 1.1. Introduction 3 1.2. Definitions 3 1.3. Geometry of the linear program 5 1.3.1. Polyhedra 5 1.3.2. Extreme points and vertices 6 1.4. Graphical solving of a linear program 6 1.5. Simplex algorithm 9 1.5.1. Basic solutions and basic feasible solutions 9 1.5.2. Simplex tableau 10 1.5.3. Change of feasible basis 11 1.5.4. Existence and uniqueness of an optimal solution 14 1.6. Initialization of the simplex algorithm 15 1.6.1. Big M method 15 1.6.2. Auxiliary program or Phase I 17 1.6.3. Degeneracy and cycling 20 1.6.4. Geometric structure of realizable solutions 21 1.7. Interior-point algorithm 22 1.8. Duality 23 1.8.1. Duality theorem 25 1.9. Relaxation 27 1.9.1. Lagrangian relaxation 27 1.10. Postoptimal analysis 29 1.10.1. Effect of modifying b 31 1.10.2. Effect of modifying c 32 1.11. Application to an inventory problem 34 1.11.1. Optimal solution 34 1.11.2. Sensitivity to variation in stock 35 1.11.3. Dual problem of the competitor 36 1.12. Using Matlab 36 Chapter 2. Integer Programming 41 2.1. Introduction 41 2.2. Solving methods 41 2.2.1. Branch-and-bound method 42 2.2.2. The branch-and-cut method 44 2.3. Binary programming 49 2.3.1. Knapsack problem 49 2.3.2. Investment problem 50 2.4. Decomposition principle 57 2.4.1. Benders decomposition 58 2.5. Using Matlab 62 Chapter 3. Dynamic Programming 65 3.1. Introduction 65 3.2. Solving strategy 66 3.3. Discrete DP 68 3.3.1. Bellman’s equation and the principle of optimality 68 3.3.2. Approach of the method 70 3.3.3. A few examples of DP 70 3.3.4. Solving an LP 73 3.3.5. Shortest path problem 74 3.3.6. Knapsack problem 79 3.3.7. Stock management problem 81 3.4. Continuous DP 83 3.4.1. Hamilton–Jacobi equation 84 3.4.2. Application to a consumption-savings model 84 3.5. Stochastic DP 85 3.5.1. Decision-chance process 85 3.5.2. Solving method 86 3.5.3. Application to a contract problem 86 3.5.4. Optimal binary search tree 87 3.6. Using Matlab 91 Chapter 4. Stochastic Programming 93 4.1. Introduction 93 4.2. Presentation of the problem 94 4.3. Optimal feedback in an open loop 94 4.4. Stochastic linear programming 95 4.4.1. Models with probability thresholds on the constraints 96 4.5. Stochastic linear programs with recourse 96 4.5.1. L-shaped method 97 4.5.2. Multicut L-shaped method 99 4.5.3. Interior linearization method 100 4.6. Nonlinear stochastic programming 100 4.6.1. Approaches to two-step problems with recourse 100 4.6.2. Regularized decomposition method 101 4.6.3. Methods based on the Lagrangian 101 4.6.4. Frank–Wolfe method for problems with simple recourse 103 4.6.5. Approximation by sampling average: Monte Carlo method 105 4.6.6. Stochastic gradient method 106 4.7. Stochastic dynamic programming 107 4.7.1. Markov decision process 108 4.7.2. Scenario tree 109 4.8. Application to the reliability of mechanical systems 111 4.8.1. Position and modeling of the reliability problem 113 4.9. Using Matlab 121 Part 2. Optimization 127 Chapter 5. Combinatorial Optimization 129 5.1. Introduction 129 5.2. Symmetric TSP 131 5.2.1. Historical overview 132 5.2.2. Solving methods 134 5.3. Asymmetric traveling salesman problem 140 5.3.1. Variants of the ATSP 140 5.3.2. Mathematical formulations 142 5.3.3. Methods for solving the ATSP 144 5.4. Vehicle routing problem 148 5.4.1. Definition 148 5.4.2. Fields of application 149 5.4.3. Parameters of the VRP 150 5.4.4. Variants of the VRP 151 5.4.5. Mathematical formulation of the VRP 153 5.4.6. Algorithmic complexity 155 5.5. Selective routing problem 156 5.5.1. Problems similar to the VRP 157 5.5.2. Mathematical formulation 157 5.6. Using Matlab 158 Chapter 6. Unconstrained Nonlinear Programming 161 6.1. Introduction 161 6.2. Mathematical formulation 161 6.2.1. Existence and uniqueness results 162 6.3. Optimality conditions 162 6.4. Quadratic problems 163 6.4.1. Gradient method with optimal step size 163 6.4.2. Conjugate gradient method 164 6.5. Newton’s algorithm 164 6.6. Methods of descent and linear search 165 6.6.1. Presentation of methods of descent 165 6.6.2. Method of greatest slope 167 6.6.3. Acceptable step size 168 6.6.4. Linear search 169 6.6.5. Newton’s method with linear search 170 6.7. Quasi-Newton methods 171 6.7.1. DFP and BFGS methods 172 6.8. Relaxation method 173 6.9. Gradient method 175 6.10. Least squares problem 176 6.10.1. Gauss–Newton method 176 6.10.2. Levenberg–Marquardt algorithm 178 6.10.3. Kalman filter 179 6.11. Direct search methods 181 6.11.1. Nelder–Mead algorithm 181 6.11.2. Torczon method 183 6.12. Application to an identification problem 183 6.13. Using Matlab 185 6.13.1. The fminsearch function 187 6.13.2. The fminunc function 188 6.13.3. Relaxation method 190 Chapter 7. Constrained Nonlinear Optimization 193 7.1. Introduction 193 7.2. Mathematical formulation 193 7.3. Lagrange multipliers 194 7.4. Optimization with inequality constraints 195 7.4.1. First-order conditions of optimality 195 7.4.2. Presentation of saddle points 197 7.4.3. Saddle point and optimization 198 7.4.4. Convex case 201 7.5. Constrained minimization algorithms 201 7.5.1. Relaxation method 202 7.5.2. Projection method 202 7.5.3. Exterior penalty method 204 7.5.4. Uzawa’s algorithm 205 7.6. Newton algorithms: SQP method 206 7.6.1. Equality constraints 207 7.6.2. Inequality constraints 209 7.7. Application to structure optimization 210 7.8. Using Matlab 217 7.8.1. The fmincon function 219 7.8.2. The fminbnd function 220 7.8.3. Penalty method 221 Appendices 229 Appendix 1. Reminders from Linear Algebra 231 Appendix 2. Reminders about functions from Rn into R 241 Appendix 3. Optimization Toolbox 245 Appendix 4. Software 249 References 253 Index 261

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