{"product_id":"engineering-optimization-isbn-9781118936337","title":"Engineering Optimization","description":"\u003cp\u003e\u003cb\u003eAn Application-Oriented Introduction to Essential Optimization Concepts and Best Practices\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOptimization 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. \u003ci\u003eEngineering Optimization\u003c\/i\u003e provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process.\u003c\/p\u003e \u003cp\u003eAlthough 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.\u003c\/p\u003e \u003cp\u003eExamples, 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.\u003c\/p\u003e \u003cp\u003eProviding excellent reference for students or professionals, \u003ci\u003eEngineering Optimization\u003c\/i\u003e:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDescribes and develops a variety of algorithms, including gradient based (such as Newton’s, and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm), along with surrogate functions for surface characterization\u003c\/li\u003e \u003cli\u003eProvides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values\u003c\/li\u003e \u003cli\u003eDetails current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling\u003c\/li\u003e \u003cli\u003eProvides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eClear 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.\u003c\/p\u003e \u003cp\u003ePreface xix \u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii \u003c\/p\u003e \u003cp\u003eNomenclature xxix \u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxxvii \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 1 Introductory Concepts 1\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e1 Optimization: Introduction and Concepts 3 \u003c\/p\u003e \u003cp\u003e2 Optimization Application Diversity and Complexity 33 \u003c\/p\u003e \u003cp\u003e3 Validation: Knowing That the Answer Is Right 53 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 2 Univariate Search Techniques 59\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e4 Univariate (Single DV) Search Techniques 61 \u003c\/p\u003e \u003cp\u003e5 Path Analysis 93 \u003c\/p\u003e \u003cp\u003e6 Stopping and Convergence Criteria: 1-D Applications 107 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 3 Multivariate Search Techniques 117\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e7 Multidimension Application Introduction and the Gradient 119 \u003c\/p\u003e \u003cp\u003e8 Elementary Gradient-Based Optimizers: \u003ci\u003eCSLS\u003c\/i\u003eand\u003ci\u003eISD\u003c\/i\u003e135 \u003c\/p\u003e \u003cp\u003e9 Second-Order Model-Based Optimizers:\u003ci\u003eSQ\u003c\/i\u003eand\u003ci\u003eNR\u003c\/i\u003e155 \u003c\/p\u003e \u003cp\u003e10 Gradient-Based Optimizer Solutions:\u003ci\u003eLM\u003c\/i\u003e, \u003ci\u003eRLM\u003c\/i\u003e, \u003ci\u003eCG\u003c\/i\u003e, \u003ci\u003eBFGS\u003c\/i\u003e, \u003ci\u003eRG\u003c\/i\u003e, and \u003ci\u003eGRG\u003c\/i\u003e173 \u003c\/p\u003e \u003cp\u003e11 Direct Search Techniques 187 \u003c\/p\u003e \u003cp\u003e12 Linear Programming 223 \u003c\/p\u003e \u003cp\u003e13 Dynamic Programming 233 \u003c\/p\u003e \u003cp\u003e14 Genetic Algorithms and Evolutionary Computation 243 \u003c\/p\u003e \u003cp\u003e15 Intuitive Optimization 253 \u003c\/p\u003e \u003cp\u003e16 Surface Analysis II 257 \u003c\/p\u003e \u003cp\u003e17 Convergence Criteria 2: N-D Applications 265 \u003c\/p\u003e \u003cp\u003e18 Enhancements to Optimizers 271 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 4 Developing Your Application Statements 279\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e19 Scaled Variables and Dimensional Consistency 281 \u003c\/p\u003e \u003cp\u003e20 Economic Optimization 289 \u003c\/p\u003e \u003cp\u003e21 Multiple OF and Constraint Applications 305 \u003c\/p\u003e \u003cp\u003e22 Constraints 319 \u003c\/p\u003e \u003cp\u003e23 Multiple Optima 335 \u003c\/p\u003e \u003cp\u003e24 Stochastic Objective Functions 353 \u003c\/p\u003e \u003cp\u003e25 Effects of Uncertainty 367 \u003c\/p\u003e \u003cp\u003e26 Optimization of Probable Outcomes and Distribution Characteristics 381 \u003c\/p\u003e \u003cp\u003e27 Discrete and Integer Variables 391 \u003c\/p\u003e \u003cp\u003e28 Class Variables 397 \u003c\/p\u003e \u003cp\u003e29 Regression 403 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 5 Perspective on Many Topics 441\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e30 Perspective 443 \u003c\/p\u003e \u003cp\u003e31 Response Surface Aberrations 459 \u003c\/p\u003e \u003cp\u003e32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints 475 \u003c\/p\u003e \u003cp\u003e33 Evaluating Optimizers 489 \u003c\/p\u003e \u003cp\u003e34 Troubleshooting Optimizers 499 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 6 Analysis of Leapfrogging Optimization 505\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e35 Analysis of Leapfrogging 507 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 7 Case Studies 529\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e36 Case Study 1: Economic Optimization of a Pipe System 531 \u003c\/p\u003e \u003cp\u003e37 Case Study 2: Queuing Study 539 \u003c\/p\u003e \u003cp\u003e38 Case Study 3: Retirement Study 543 \u003c\/p\u003e \u003cp\u003e39 Case Study 4: A\u003ci\u003eGoddard\u003c\/i\u003e Rocket Study 551 \u003c\/p\u003e \u003cp\u003e40 Case Study 5: Reservoir 557 \u003c\/p\u003e \u003cp\u003e41 Case Study 6: Area Coverage 561 \u003c\/p\u003e \u003cp\u003e42 Case Study 7: Approximating Series Solution to an ODE 565 \u003c\/p\u003e \u003cp\u003e43 Case Study 8: Horizontal Tank Vapor–Liquid Separator 571 \u003c\/p\u003e \u003cp\u003e44 Case Study 9: In Vitro Fertilization 579 \u003c\/p\u003e \u003cp\u003e45 Case Study 10: Data Reconciliation 585 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 8 Appendices 591\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 9 References and Index 717\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eReferences and Additional Resources 719 \u003c\/p\u003e \u003cp\u003eIndex 723\u003c\/p\u003e   \u003cp\u003e \u003cb\u003eR. Russell Rhinehart\u003c\/b\u003e is an Emeritus Professor and Amoco Chair in the School of Chemical Engineering at  Oklahoma State University. He was named as one of InTECH's 50 Most Influential Industry Innovators in 2004,  and was inducted into the Automation Hall of Fame for the Process Industries in 2005. His research focuses on  process improvement through modeling, optimization and control, and product improvement through modeling  and design.      \u003c\/p\u003e\u003cp\u003e \u003cb\u003eAn Application-Oriented Introduction to  Essential Optimization Concepts and Best Practices\u003c\/b\u003e   \u003c\/p\u003e\u003cp\u003e 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. \u003ci\u003eEngineering Optimization\u003c\/i\u003e provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout  each stage of the optimization process.   \u003c\/p\u003e\u003cp\u003e 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.  \t \u003c\/p\u003e\u003cp\u003eExamples, 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. \t \u003c\/p\u003e\u003cp\u003eProviding excellent reference for students or professionals, \u003ci\u003eEngineering Optimization\u003c\/i\u003e:  \u003c\/p\u003e\u003cul\u003e \u003cli\u003eDescribes and develops a variety of algorithms, including gradient based (such as Newton's,  and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm),  along with surrogate functions for surface characterization\u003c\/li\u003e \u003cli\u003eProvides guidance on optimizer choice by application, and explains how to determine appropriate  optimizer parameter values\u003c\/li\u003e \u003cli\u003eDetails current best practices for critical stages of specifying an optimization procedure, including  decision variables, defining constraints, and relationship modeling\u003c\/li\u003e \u003cli\u003eProvides access to software and Visual Basic macros for Excel on the companion website, along with  solutions to examples presented in the book\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e  \u003cp\u003e 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.\u003c\/p\u003e","brand":"Wiley-ASME Press Series","offers":[{"title":"Default Title","offer_id":47989137866981,"sku":"NP9781118936337","price":106.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118936337.jpg?v=1761782948","url":"https:\/\/k12savings.com\/products\/engineering-optimization-isbn-9781118936337","provider":"K12savings","version":"1.0","type":"link"}