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Engineering Optimization
Applications, Methods and Analysis
R. Russell Rhinehart (Author)
9781118936337, Wiley
Hardback, published 11 April 2018
784 pages
23.9 x 19.8 x 4.8 cm, 1.61 kg
An Application-Oriented Introduction to Essential Optimization Concepts and Best Practices Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process. Although essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project. Examples, exercises, and homework throughout reinforce the author’s “do, not study” approach to learning, underscoring the application-oriented discussion that provides a deep, generic understanding of the optimization process that can be applied to any field. Providing excellent reference for students or professionals, Engineering Optimization: Clear explanations, explicit equation derivations, and practical examples make this book ideal for use as part of a class or self-study, assuming a basic understanding of statistics, calculus, computer programming, and engineering models. Anyone seeking best practices for “making the best choices” will find value in this introductory resource.
Preface xix Acknowledgments xxvii Nomenclature xxix About the Companion Website xxxvii Section 1 Introductory Concepts 1 1 Optimization: Introduction and Concepts 3 2 Optimization Application Diversity and Complexity 33 3 Validation: Knowing That the Answer Is Right 53 Section 2 Univariate Search Techniques 59 4 Univariate (Single DV) Search Techniques 61 5 Path Analysis 93 6 Stopping and Convergence Criteria: 1-D Applications 107 Section 3 Multivariate Search Techniques 117 7 Multidimension Application Introduction and the Gradient 119 8 Elementary Gradient-Based Optimizers: CSLSandISD135 9 Second-Order Model-Based Optimizers:SQandNR155 10 Gradient-Based Optimizer Solutions:LM, RLM, CG, BFGS, RG, and GRG173 11 Direct Search Techniques 187 12 Linear Programming 223 13 Dynamic Programming 233 14 Genetic Algorithms and Evolutionary Computation 243 15 Intuitive Optimization 253 16 Surface Analysis II 257 17 Convergence Criteria 2: N-D Applications 265 18 Enhancements to Optimizers 271 Section 4 Developing Your Application Statements 279 19 Scaled Variables and Dimensional Consistency 281 20 Economic Optimization 289 21 Multiple OF and Constraint Applications 305 22 Constraints 319 23 Multiple Optima 335 24 Stochastic Objective Functions 353 25 Effects of Uncertainty 367 26 Optimization of Probable Outcomes and Distribution Characteristics 381 27 Discrete and Integer Variables 391 28 Class Variables 397 29 Regression 403 Section 5 Perspective on Many Topics 441 30 Perspective 443 31 Response Surface Aberrations 459 32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints 475 33 Evaluating Optimizers 489 34 Troubleshooting Optimizers 499 Section 6 Analysis of Leapfrogging Optimization 505 35 Analysis of Leapfrogging 507 Section 7 Case Studies 529 36 Case Study 1: Economic Optimization of a Pipe System 531 37 Case Study 2: Queuing Study 539 38 Case Study 3: Retirement Study 543 39 Case Study 4: AGoddard Rocket Study 551 40 Case Study 5: Reservoir 557 41 Case Study 6: Area Coverage 561 42 Case Study 7: Approximating Series Solution to an ODE 565 43 Case Study 8: Horizontal Tank Vapor–Liquid Separator 571 44 Case Study 9: In Vitro Fertilization 579 45 Case Study 10: Data Reconciliation 585 Section 8 Appendices 591 Section 9 References and Index 717 References and Additional Resources 719 Index 723
Subject Areas: Mathematics [PB]
