Introduction to Applied Optimization (Springer Optimization and Its Applications Book 22)

Preface -- 1. Introduction -- 2. Linear Programming -- 3. Nonlinear Programming -- 4. Discrete Optimization -- 5. Optimization under Uncertainty -- 6. Multi-objective Optimization -- 7. Optimal Control and Dynamic Optimization -- Index;This text presents a multi-disciplined view of optimization, providing students and researchers with a thorough examination of algorithms, methods, and tools from diverse areas of optimization without introducing excessive theoretical detail. This second edition includes additional topics, including global optimization and a real-world case study using important concepts from each chapter. Key Features: Provides well-written self-contained chapters, including problem sets and exercises, making it ideal for the classroom setting; Introduces applied optimization to the hazardous waste blending problem; Explores linear programming, nonlinear programming, discrete optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control and stochastic optimal control; Includes an extensive bibliography at the end of each chapter and an index; GAMS files of case studies for Chapters 2, 3, 4, 5, and 7 are linked to http://www.springer.com/math/book/978-0-387-76634-8; Solutions manual available upon adoptions. Introduction to Applied Optimization is intended for advanced undergraduate and graduate students and will benefit scientists from diverse areas, including engineers Optimization has pervaded all spheres of human endeavor. Although op- mization has been practiced in some form or other from the early prehistoric era, this area has seen progressive growth during the last?ve decades. M- ern society lives not only in an environment of intense competition but is also constrained to plan its growth in a sustainable manner with due concern for conservation of resources. Thus, it has become imperative to plan, design, operate, and manage resources and assets in an optimal manner. Early - proaches have been to optimize individual activities in a standalone manner, however,thecurrenttrendistowardsanintegratedapproach:integratings- thesis and design, design and control, production planning, scheduling, and control. The functioning of a system may be governed by multiple perf- mance objectives. Optimization of such systems will call for special strategies for handling the multiple objectives to provide solutions closer to the systems requirement. Uncertainty and variability are two issues which render op- mal decision making di?cult. Optimization under uncertainty would become increasingly important if one is to get the best out of a system plagued by uncertain components. These issues have thrown up a large number of ch- lenging optimization problems which need to be resolved with a set of existing and newly evolving optimization tools. Optimization theory had evolved initially to provide generic solutions to optimizationproblemsinlinear,nonlinear,unconstrained,andconstrained- mains. Theseoptimization problems wereoften called mathematical progr- mingproblemswithtwodistinctiveclassi?cations,namelylinearandnonlinear programming problems.

this Text Presents A Multi-disciplined View Of Optimization, Providing Students And Researchers With A Thorough Examination Of Algorithms, Methods, And Tools From Diverse Areas Of Optimization Without Introducing Excessive Theoretical Detail. This Second Edition Includes Additional Topics, Including Global Optimization And A Real-world Case Study Using Important Concepts From Each Chapter.

key Features:

• provides Well-written Self-contained Chapters, Including Problem Sets And Exercises, Making It Ideal For The Classroom Setting;
• introduces Applied Optimization To The Hazardous Waste Blending Problem;
• explores Linear Programming, Nonlinear Programming, Discrete Optimization, Global Optimization, Optimization Under Uncertainty, Multi-objective Optimization, Optimal Control And Stochastic Optimal Control;
• includes An Extensive Bibliography At The End Of Each Chapter And An Index;
• gams Files Of Case Studies For Chapters 2, 3, 4, 5, And 7 Are Linked To Http://www.springer.com/math/book/978-0-387-76634-8;
• solutions Manual Available Upon Adoptions.

introduction To Applied Optimization Is Intended For Advanced Undergraduate And Graduate Students And Will Benefit Scientists From Diverse Areas, Including Engineers.

Front Matter....Pages 1-20 Introduction....Pages 1-10 Linear Programming....Pages 1-29 Nonlinear Programming....Pages 1-36 Discrete Optimization....Pages 1-48 Optimization Under Uncertainty....Pages 1-54 Multiobjective Optimization....Pages 1-36 Optimal Control and Dynamic Optimization....Pages 1-63 Back Matter....Pages 1-13 The author provides scientists, researchers and analysts in various fields with opportunities to identify and apply algorithms, methods and tools from the diverse areas of optimization to their own field without getting into too much detail about the underlying theories