Introduction to Applied Optimization (eBook)

The wide scope of optimization mandates extensive interaction between various disciplines in the development of the methods and algorithms, and in their fruitful application to real-world problems. This book presents a discipline-independent view of optimization, providing opportunities for students to identify and apply algorithms, methods, and tools from the diverse areas of optimization to their own fields without getting into too much detail about the underlying theories. The second edition of this book includes two new chapters: a chapter on global optimization and a real-world case study that uses principles from each chapter. Key Features: (1) Provides self-contained chapters, including problem sets and exercises; (2) Introduces applied optimization with several unique applications, i.e., hazardous waste blending problem; (3) Explores a number or important methods, i.e., the simplex method, weighting method, constraint method, and goal programming method; (4) Explores several different types of optimization, i.e., discrete, global, multi-objective, and dynamic optimization; (5) Includes an extensive bibliography at the end of each chapter. This book is intended for a variety of scientists, engineers, researchers, and advanced students interested in applied optimization.

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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.