Handbook of Applied Optimization

Handbook of Applied Optimization

by Panos M. Pardalos
     
 

ISBN-10: 0195125940

ISBN-13: 9780195125948

Pub. Date: 02/22/2002

Publisher: Oxford University Press, USA


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

Overview


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.

Product Details

ISBN-13:
9780195125948
Publisher:
Oxford University Press, USA
Publication date:
02/22/2002
Pages:
2026
Product dimensions:
10.10(w) x 7.40(h) x 2.20(d)

Table of Contents

Preface Panos M. Pardalos and Mauricio G. C. Resende
Introduction Panos M. Pardalos and Mauricio G. C. Resende
Part One: Algorithms
1. Linear Programming
1.1. Introduction, Tamás Terlaky
1.2. Simplex-Type Algorithms, Tamás Terlaky
1.3. Interior-Point Methods for Linear Optimization, Kees Roos
2. Semidefinite Programming, Henry Wolkowicz
3. Combinatorial Optimization
3.1. Introduction, Panos M. Pardalos and Mauricio G. C. Resende
3.2. Branch-and-Bound Methods, Eva K. Lee
3.3. Branch-and-Cut Algorithms for Combinatorial Optimization Problems, John E. Mitchell
3.4. Dynamic Programming Approaches, Augustine O. Esogbue
3.5. Local Search, Mutsunori Yagiura and Toshihide Ibaraki
3.6. Metaheuristics
3.6.1. Introduction, Bruce L. Golden and Edward A. Wasil
3.6.2. Ant Systems, Éric D. Taillard
3.6.3. Population Heuristics, John E. Beasley
3.6.4. Memetic Algorithms, Pablo Moscato
3.6.5. Greedy Randomized Adaptive Search Procedures, Leonidas S. Pitsoulis and Mauricio G. C. Resende
3.6.6. Scatter Search, Manuel Laguna
3.6.7. Tabu Search, Fred Glover and Manuel Laguna
3.6.8. Simulated Annealing, E. H. L. Aarts and H. M. M. Ten Eikelder
3.6.9. Variable Neighborhood Search, Pierre Hansen and Nenad Mladenovi?
4. Quadratic Programming, Yinyu Ye
5. Nonlinear Programming
5.1. Introduction, Gianni Di Pillo and Laura Palagi
5.2. Unconstrained Nonlinear Programming, Gianni Di Pillo and Laura Palagi
5.3. Constrained Nonlinear Programming }a Gianni Di Pillo and Laura Palagi
5.4. Nonsmooth Optimization, Manlio Gaudioso
6. Deterministic Global Optimizatio and Its Applications, Christodoulos A. Floudas
7. Decomposition Methods for Mathematical Programming, Philippe Mahey
8. Network Optimization
8.1. Introduction, Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin
8.2. Maximum Flow Problem, Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin
8.3. Shortest-Path Algorithms, Edith Cohen
8.4. Minimum-Cost Single-Commodity Flow, S. Thomas McCormick
8.5. Minimum-Cost Multicommodity Flow, Pierre Chardaire and Abdel Lisser
8.6. Minimum Spanning Tree Problem, Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin
9. Integer Programming
9.1. Introduction, Nelson Maculan
9.2. Linear 0-1 Programming, Nelson Maculan
9.3. Psedo-Boolean Optimization, Yves Crama and peter L. Hammer
9.4. Mixed-Integer Nonlinear Optimization, Christodoulos A. Floudas
9.5. Lagrangian Relaxation, Monique Guignard
9.6. Heuristics for 0-1 Mixed-Integer Programming, Arne Løokketangen
10. Artificial Neural Networks in Optimization and Applications, Theodore B. Trafalis and Suat Kasap
11. Stochastic Programming, John R. Birge
12. Hierarchical Optimization, Hoang Tuy
13. Complementarity and Related Problems, Michael C. Ferris and Christian Kanzow
14. Data Envelopment Analysis, José H. Dulá
15. Parallel Algorithms in Optimization, Yair Censor and Stavros A. Zenios
16. Randomization in Discrete Optimization: Annealing Algorithms, Sanguthevar Rajasekaran
Part Two: Applications
17. Problem Types
17.1. Optimization and Heuristics of Scheduling, Chung-Yee Lee and Michael Pinedo
17.2. The Vehicle Routing Problem, John E. Beasley, Abilio Lucena, and Marcus Poggi de Aragão
17.3. Network Designs: Approximations for Steiner Minimum Trees, Ding-Zhu Du
17.4. Approximate Solutions to Bin Packing Problems, Edward G. Coffman, Jr., János Csirik, and Gerhard J. Woeginger
17.5. The Traveling Salesmand Problem, Rainer E. Burkard
17.6. Inventory Management, Dukwon Kim and Boghos D. Sivazlian
17.7. Location, Zvi Drezner
17.8. Algorithms for the Satisfiability (SAT) Problem, Jun Gu, Paul W. Purdom, John Franco, and Benjamin W. Wah
17.9. Assignment Problems, Eranda Çela
18. Application Areas
18.1. Transportation and Logistics, Warren B. Powell
18.2. Airline Optimization, Gang Yu and Benjamin G. Thengvall
18.3. Optimization in the Rail Industry, Alexandra M. Newman, Linda K. Nozick, and Candace Arai Yano
18.4. Forstry Industry, Andrés Weintraub Pohorille and John Hof
18.5. Manufacturing Planning and Control, Stephen C. Graves
18.6. Semiconductor Production Planning, Robert C. Leachman
18.7. Optimization in the Aerospace Industry, Matthew E. Berge, John T. Betts, Sharon K. Filipowski, William P. Huffman, and David P. Young
18.8. Energy
18.8.1. Optimization in Electrical Power Systems, Gerson Couto de Oliveira, Sergio Granville, and Mario Pereira
18.8.2. Optimization Applications in Oil and Gas Recovery, Roland N. Horne
18.8.3. Natural Gas Pipeline Optimization, Roger Z. Rios-Mercado
18.9. Opimization of Telecommunications Networks, G. Anandalingam
18.10. Optimization of Test Intervals in Nuclear Engineering, Stanislav Uryasev
18.11. Optimization in VLSI Design: Target Distance Models for Cell Placement, Hussein A. Y. Etawil and Anthony Vannelli
18.12. Optimization Models in Transportation Planning, Michael Florian and Donald W. Hearn
18.13. Optimization in computation Molecular Biology, Guoliang Xue
18.14. Optimization in the Financial Services Industry, Anna Nagurney
18.15. Applied Large-Scale Nonlinear Optimization for Optimal Control of Partial Differential Equations and Differential Algebraic Equations, J. B. Rosen, John H. Glick, and E. Michael Gertz
18.16. Optimization in Water Reservoir Systems, Kumaraswamy Ponnambalam
18.17. Optimization Problems in Air-Pollution Modeling, Ivan Dimov and Zahari Zlatev
18.18. Applied Optimization in Agriculture, Charles B. Moss
18.19. Optimization in Graph Drawing, Petra Mutzel
18.20. Optimization for Modeling of Nonlinear Interactions in Mechanics, G. E. Stavroulakis
Part Three: Software
19. Optimization Modeling Languages, Emmanuel Fragnière and Jacek Gondzio
20. Optimization Software Packages, Stephen J. Wright
21. Optimization Software Libraries, Andreas Fink, Stefan VoB, and David L. Woodruff
22. Optimization Test Problem Libraries, John E. Beasley
23. Parallel Computing Environment, Simone de L. Martins, Celso C. Ribeiro, and Noemi Rodriguez
24. Experimental Analysis of Optimization Algorithms, Catherine C. McGeoch
25. Object-Oriented Programming, Andreas Fink, Stefan VoB, and David L. Woodruff
26. Optimization and the Internet, Michael A. Trick
Directory of Contributors
Index

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