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This book attempts to present the concepts which underlie the various optimization procedures which are commonly used. It is written primarily for those scientists such as economists, operations researchers, and en­ gineers whose main tools of analysis involve optimization techniques and who possess a (not very sharp) knowledge of one or one-and-a-half year's calculus through partial differentiation and Taylor's theorem and some acquaintance with elementary vector and matrix terminology. Such a scientist is frequently confronted with expressions such as Lagrange multi­ pliers, first-and second-order conditions, linear programming and activity analysis, duality, the Kuhn-Tucker conditions, and, more recently, dy­ namic programming and optimal control. He or she uses or needs to use these optimization techniques, and would like to feel more comfortable with them through better understanding of their underlying mathematical concepts, but has no immediate use for a formal theorem-proof treatment which quickly abstracts to a general case of n variables and uses a style and terminology that are discouraging to people who are not mathematics majors. The emphasis of this book is on clarity and plausibility. Through examples which are worked out step by step in detail, I hope to illustrate some tools which will be useful to scientists when they apply optimization techniques to their problems. Most of the chapters may be read independently of each other-with the exception of Chapter 6, which depends on Chapter 5. For instance, the reader will find little or no difficulty in reading Chapter 8 without having read the previous chapters.

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It is vitally important to meet or exceed previous quality and reliability standards while at the same time reducing resource consumption. This textbook addresses this critical imperative integrating theory, modeling, the development of numerical methods, and problem solving, thus preparing the student to apply optimization to real-world problems. This text covers a broad variety of optimization problems using: unconstrained, constrained, gradient, and non-gradient techniques; duality concepts; multiobjective optimization; linear, integer, geometric, and dynamic programming with applications; and finite element-based optimization. In this revised and enhanced second edition of Optimization Concepts and Applications in Engineering, the already robust pedagogy has been enhanced with more detailed explanations, an increased number of solved examples and end-of-chapter problems. The source codes are now available free on multiple platforms. It is ideal for advanced undergraduate or graduate courses and for practicing engineers in all engineering disciplines, as well as in applied mathematics.

It is vitally important to meet or exceed previous quality and reliability standards while at the same time reducing resource consumption. This textbook addresses this critical imperative integrating theory, modeling, the development of numerical methods, and problem solving, thus preparing the student to apply optimization to real-world problems. This text covers a broad variety of optimization problems using: unconstrained, constrained, gradient, and non-gradient techniques; duality concepts; multiobjective optimization; linear, integer, geometric, and dynamic programming with applications; and finite element-based optimization. In this revised and enhanced second edition of Optimization Concepts and Applications in Engineering, the already robust pedagogy has been enhanced with more detailed explanations, an increased number of solved examples and end-of-chapter problems. The source codes are now available free on multiple platforms. It is ideal for advanced undergraduate or graduate courses and for practicing engineers in all engineering disciplines, as well as in applied mathematics.

A thorough and highly accessible resource for analysts in a broad range of social sciences. Optimization: Foundations and Applications presents a series of approaches to the challenges faced by analysts who must find the best way to accomplish particular objectives, usually with the added complication of constraints on the available choices. Award-winning educator Ronald E. Miller provides detailed coverage of both classical, calculus-based approaches and newer, computer-based iterative methods. Dr. Miller lays a solid foundation for both linear and nonlinear models and quickly moves on to discuss applications, including iterative methods for root-finding and for unconstrained maximization, approaches to the inequality constrained linear programming problem, and the complexities of inequality constrained maximization and minimization in nonlinear problems. Other important features include: More than 200 geometric interpretations of algebraic results, emphasizing the intuitive appeal of mathematics Classic results mixed with modern numerical methods to aid users of computer programs Extensive appendices containing mathematical details important for a thorough understanding of the topic With special emphasis on questions most frequently asked by those encountering this material for the first time, Optimization: Foundations and Applications is an extremely useful resource for professionals in such areas as mathematics, engineering, economics and business, regional science, geography, sociology, political science, management and decision sciences, public policy analysis, and numerous other social sciences. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

Inbound call center problems need decisions in four main areas; the number of incoming calls, necessary number of agents to handle these calls, shift starting times for the operations and the number of agents to be allocated to each shift. Call center models mainly cover these areas seperately. This study approaches to the problem with an integrated Markovian approximation, heuristic supported simulation and optimization model. Incoming demand data is adapted from a private Turkish Bank and an application of the model is conducted. Moreover, probabilistic lunch breaks are introduced to the optimization part of the model. Finally, the model can be used as an easy decision support tool for the call center managers.

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Inbound call center problems need decisions in four main areas; the number of incoming calls, necessary number of agents to handle these calls, shift starting times for the operations and the number of agents to be allocated to each shift. Call center models mainly cover these areas seperately. This study approaches to the problem with an integrated Markovian approximation, heuristic supported simulation and optimization model. Incoming demand data is adapted from a private Turkish Bank and an application of the model is conducted. Moreover, probabilistic lunch breaks are introduced to the optimization part of the model. Finally, the model can be used as an easy decision support tool for the call center managers.

A basic text for engineering students and practicing engineers dealing with design problems in all engineering disciplines. Optimization algorithms are developed through illustrative examples. Includes numerical results on the efficiencies of various algorithms, comparison of constrained-optimization methods, and strategies for optimization studies. Also includes several actual case studies.

This instructive book introduces the key ideas behind practical nonlinear optimization, accompanied by computational examples and supporting software. It combines computational finance with an important class of numerical techniques.

This instructive book introduces the key ideas behind practical nonlinear optimization, accompanied by computational examples and supporting software. It combines computational finance with an important class of numerical techniques.

Entropy optimization has become a powerful tool for data analysis and problem solving that has an infinite array of real-world applications. This senior-level textbook provides a unified conceptual framework for the study of probabilistic systems with its elucidation of three key concepts: Shannon's Information Theory, Jayne's Maximum Entropy Principle, and Kullback's Minimum Cross-Entropy Principle. A wide array of real-world problems and applications are included that will establish the usefulness of these methods for any discipline looking at probabilistic systems and information (such as engineering, statistics, economics, and operations research). This textbook, complete with exercises, will leave students with the ability to apply these principles to new problems. The first true textbook that provides an interdisciplinary approach to entropy optimization principles with numerous applications and exercisesApplies principles to a diverse assortment of applications in statistics, thermodynamics, pattern recognition, spectral analysis, queuing theory, and parameter estimation problemsWill be of use to all engineering students looking at probabilistic systems, as well as to students of statistics, operations research and economics

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Filling a void in chemical engineering and optimization literature, this book presents the theory and methods for nonlinear and mixed-integer optimization, and their applications in the important area of process synthesis. Other topics include modeling issues in process synthesis, and optimization-based approaches in the synthesis of heat recovery systems, distillation-based systems, and reactor-based systems. The basics of convex analysis and nonlinear optimization are also covered and the elementary concepts of mixed-integer linear optimization are introduced. All chapters have several illustrations and geometrical interpretations of the material as well as suggested problems. Nonlinear and Mixed-Integer Optimization will prove to be an invaluable source--either as a textbook or a reference--for researchers and graduate students interested in continuous and discrete nonlinear optimization issues in engineering design, process synthesis, process operations, applied mathematics, operations research, industrial management, and systems engineering.