Handbook of Applied Optimization

ISBN 13: 9780195125948

Handbook of Applied Optimization

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9780195125948: Handbook of Applied Optimization
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Optimization is an essential tool in every project in every large-scale organization, whether in business, industry, engineering, and science. In recent years, algorithmic advances and software and hardware improvements have given managers a powerful framework for making key decisions about everything from production planning to scheduling distribution.
This comprehensive resource brings together in one volume the major advances in the field. Distinguished contributors focus on the algorithmic and computational aspects of optimization, particularly the most recent methods for solving a wide range of decision-making problems.
The book is divided into three main sections: algorithms, covering every type of programming; applications, where computational tools are put to work solving tasks in planning, production, distribution, scheduling and other decisions in project management; and software, a comprehensive introduction to languages and systems.
Designed as a practical resource for programmers and project planners and managers, it covers optimization problems in a wide range of settings, from the airline and aerospace industries to telecommunications, finance, health systems, biomedicine, and engineering.

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About the Author:

Panos M. Pardalos is at University of Florida, Gainesville. Mauricio G. C. Resende is at ATandT Laboratories, New Jersey.

Review:

"The editors draw on the expertise of researchers and application specialists from throughout the world."--Choice

"This reference provides a guide for applications specialists to the most important instruments and the major recent advances in the field of applied optimization. Pardalos (industrial and systems engineering, U. of Florida) and Resende (research scientist, ATandT Laboratories) present 26
chapters in which expert contributors discuss algorithms (linear, semidefinite, quadratic, nonlinear, stochastic, and integer programming), combinatorial optimization, deterministic global optimization, decomposition methods for mathematical programming, network and hierarchical optimization,
artificial neural networks and parallel algorithms in optimization, complementary and related problems, data envelopment analysis, and randomization in discrete optimization. They also cover applications (problem types, application areas) and software."--SciTech Book News

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