Handbook of Applied Optimization by Panos M. Pardalos

Handbook of Applied Optimization

EditorPanos M. Pardalos, Mauricio G. C. Resende

Hardcover | March 15, 2001

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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 abouteverything 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 introductionto 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.

About The Author

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

Title:Handbook of Applied OptimizationFormat:HardcoverPublished:March 15, 2001Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0195125940

ISBN - 13:9780195125948

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Table of Contents

PrefacePanos M. Pardalos and Mauricio G. C. Resende: IntroductionPanos M. Pardalos and Mauricio G. C. Resende: Part One: Algorithms1. Linear Programming1.1. Tamas Terlaky: Introduction1.2. Tamas Terlaky: Simplex-Type Algorithms1.3. Kees Roos: Interior-Point Methods for Linear Optimization2. Henry Wolkowicz: Semidefinite Programming3. Combinatorial Optimization3.1. Panos M. Pardalos and Mauricio G. C. Resende: Introduction3.2. Eva K. Lee: Branch-and-Bound Methods3.3. John E. Mitchell: Branch-and-Cut Algorithms for Combinatorial Optimization Problems3.4. Augustine O. Esogbue: Dynamic Programming Approaches3.5. Mutsunori Yagiura and Toshihide Ibaraki: Local Search3.6. Metaheuristics3.6.1. Bruce L. Golden and Edward A. Wasil: Introduction3.6.2. Eric D. Taillard: Ant Systems3.6.3. John E. Beasley: Population Heuristics3.6.4. Pablo Moscato: Memetic Algorithms3.6.5. Leonidas S. Pitsoulis and Mauricio G. C. Resende: Greedy Randomized Adaptive Search Procedures3.6.6. Manuel Laguna: Scatter Search3.6.7. Fred Glover and Manuel Laguna: Tabu Search3.6.8. E. H. L. Aarts and H. M. M. Ten Eikelder: Simulated Annealing3.6.9. Pierre Hansen and Nenad Mladenovic: Variable Neighborhood Search4. Yinyu Ye: Quadratic Programming5. Nonlinear Programming5.1. Gianni Di Pillo and Laura Palagi: Introduction5.2. Gianni Di Pillo and Laura Palagi: Unconstrained Nonlinear Programming5.3. Constrained Nonlinear Programming }a Gianni Di Pillo and Laura Palagi5.4. Manlio Gaudioso: Nonsmooth Optimization6. Christodoulos A. Floudas: Deterministic Global Optimizatio and Its Applications7. Philippe Mahey: Decomposition Methods for Mathematical Programming8. Network Optimization8.1. Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin: Introduction8.2. Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin: Maximum Flow Problem8.3. Edith Cohen: Shortest-Path Algorithms8.4. S. Thomas McCormick: Minimum-Cost Single-Commodity Flow8.5. Pierre Chardaire and Abdel Lisser: Minimum-Cost Multicommodity Flow8.6. Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin: Minimum Spanning Tree Problem9. Integer Programming9.1. Nelson Maculan: Introduction9.2. Nelson Maculan: Linear 0-1 Programming9.3. Yves Crama and peter L. Hammer: Psedo-Boolean Optimization9.4. Christodoulos A. Floudas: Mixed-Integer Nonlinear Optimization9.5. Monique Guignard: Lagrangian Relaxation9.6. Arne Lookketangen: Heuristics for 0-1 Mixed-Integer Programming10. Theodore B. Trafalis and Suat Kasap: Artificial Neural Networks in Optimization and Applications11. John R. Birge: Stochastic Programming12. Hoang Tuy: Hierarchical Optimization13. Michael C. Ferris and Christian Kanzow: Complementarity and Related Problems14. Jose H. Dula: Data Envelopment Analysis15. Yair Censor and Stavros A. Zenios: Parallel Algorithms in Optimization16. Sanguthevar Rajasekaran: Randomization in Discrete Optimization: Annealing AlgorithmsPart Two: Applications17. Problem Types17.1. Chung-Yee Lee and Michael Pinedo: Optimization and Heuristics of Scheduling17.2. John E. Beasley, Abilio Lucena, and Marcus Poggi de Aragao: The Vehicle Routing Problem17.3. Ding-Zhu Du: Network Designs: Approximations for Steiner Minimum Trees17.4. Edward G. Coffman, Jr., Janos Csirik, and Gerhard J. Woeginger: Approximate Solutions to Bin Packing Problems17.5. Rainer E. Burkard: The Traveling Salesmand Problem17.6. Dukwon Kim and Boghos D. Sivazlian: Inventory Management17.7. Zvi Drezner: Location17.8. Jun Gu, Paul W. Purdom, John Franco, and Benjamin W. Wah: Algorithms for the Satisfiability (SAT) Problem17.9. Eranda Cela: Assignment Problems18. Application Areas18.1. Warren B. Powell: Transportation and Logistics18.2. Gang Yu and Benjamin G. Thengvall: Airline Optimization18.3. Alexandra M. Newman, Linda K. Nozick, and Candace Arai Yano: Optimization in the Rail Industry18.4. Andres Weintraub Pohorille and John Hof: Forstry Industry18.5. Stephen C. Graves: Manufacturing Planning and Control18.6. Robert C. Leachman: Semiconductor Production Planning18.7. Matthew E. Berge, John T. Betts, Sharon K. Filipowski, William P. Huffman, and David P. Young: Optimization in the Aerospace Industry18.8. Energy18.8.1. Gerson Couto de Oliveira, Sergio Granville, and Mario Pereira: Optimization in Electrical Power Systems18.8.2. Roland N. Horne: Optimization Applications in Oil and Gas Recovery18.8.3. Roger Z. Rios-Mercado: Natural Gas Pipeline Optimization18.9. G. Anandalingam: Opimization of Telecommunications Networks18.10. Stanislav Uryasev: Optimization of Test Intervals in Nuclear Engineering18.11. Hussein A. Y. Etawil and Anthony Vannelli: Optimization in VLSI Design: Target Distance Models for Cell Placement18.12. Michael Florian and Donald W. Hearn: Optimization Models in Transportation Planning18.13. Guoliang Xue: Optimization in computation Molecular Biology18.14. Anna Nagurney: Optimization in the Financial Services Industry18.15. J. B. Rosen, John H. Glick, and E. Michael Gertz: Applied Large-Scale Nonlinear Optimization for Optimal Control of Partial Differential Equations and Differential Algebraic Equations18.16. Kumaraswamy Ponnambalam: Optimization in Water Reservoir Systems18.17. Ivan Dimov and Zahari Zlatev: Optimization Problems in Air-Pollution Modeling18.18. Charles B. Moss: Applied Optimization in Agriculture18.19. Petra Mutzel: Optimization in Graph Drawing18.20. G. E. Stavroulakis: Optimization for Modeling of Nonlinear Interactions in MechanicsPart Three: Software19. Emmanuel Fragniere and Jacek Gondzio: Optimization Modeling Languages20. Stephen J. Wright: Optimization Software Packages21. Andreas Fink, Stefan VoB, and David L. Woodruff: Optimization Software Libraries22. John E. Beasley: Optimization Test Problem Libraries23. Simone de L. Martins, Celso C. Ribeiro, and Noemi Rodriguez: Parallel Computing Environment24. Catherine C. McGeoch: Experimental Analysis of Optimization Algorithms25. Andreas Fink, Stefan VoB, and David L. Woodruff: Object-Oriented Programming26. Michael A. Trick: Optimization and the InternetDirectory of ContributorsIndex

Editorial Reviews

"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 26chapters 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