Introduction to Applied Optimization / Edition 2

by Urmila Diwekar

Overview

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 five 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 difficult. 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- mingproblemswithtwodistinctiveclassifications,namelylinearandnonlinear programming problems.

Product Details

ISBN-13:	9780387766348
Publisher:	Springer US
Publication date:	08/28/2008
Series:	Springer Optimization and Its Applications , #22
Edition description:	2nd ed. 2008
Pages:	291
Product dimensions:	6.10(w) x 9.30(h) x 0.80(d)

Table of Contents

Linear Programming.- Nonlinear Programming.- Discrete Optimization.- Optimization Under Uncertainty.- Multiobjective Optimization.- Optimal Control and Dynamic Optimization.

Editorial Reviews

From reviews:

"The book is well written and the presentation is rigorous and self-contained. The book will be of interest to researchers in various fields as well as undergraduate and graduate students in engineering sciences, management science, and decision science.”

—I. M. Stancu-Minasian, Zentralblatt MATH, Vol. 1043 (18), 2004

"Urmila Diwekar’s book on applied optimization is one of the few books on the subject that combines impressive breadth of coverage with delightful readability. In her exposition of concepts and algorithms in the major areas of optimization, she always goes to the heart of the matter and illustrates her explanations with simple diagrams and numerical examples. Graduate and undergraduate students, who constitute part of the target audience, should find this a very useful book.”

—Jamshed A. Modi, Interfaces, Vol. 36 (1), 2006

"The book is well-written, and . . . it covers fairly significant territory, in a relatively slim volume. This work is definitely a welcome addition to the existing optimization literature, given its emphasis on modeling and solution practice, as well as its ‘user-friendly’ style of exposition.”

—János D. Pintér, European Journal of Operations Research, Vol. 177, 2007

From the reviews of the second edition:

"Diwekar’s book covers a wide variety of optimization topics, including some recent developments, in under 300 pages. … The book would be suitable as a textbook; it contains numerous exercises and a solutions manual is available." (John D. Cook, MAA Online, February, 2009)

From the Publisher