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Optimization Software Class Libraries

  • Book
  • © 2002

Overview

Editors:
  1. Stefan Voß

    1. Braunschweig University of Technology, Germany

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    You can also search for this editor in PubMed   Google Scholar

  2. David L. Woodruff

    1. University of California, Davis, USA

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Operations Research/Computer Science Interfaces Series (ORCS, volume 18)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-x
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  2. Optimization Software Class Libraries

    • Stefan Voß, David L. Woodruff
    Pages 1-24

  3. Distribution, Cooperation, and Hybridization for Combinatorial Optimization

    • Martin S. Jones, Geoff P. McKeown, Vic J. Rayward-Smith
    Pages 25-58

  4. A Framework for Local Search Heuristics for Combinatorial Optimization Problems

    • Alexandre A. Andreatta, Sérgio E. R. Carvalho, Celso C. Ribeiro
    Pages 59-79

  5. Hotframe: A Heuristic Optimization Framework

    • Andreas Fink, Stefan Voß
    Pages 81-154

  6. Writing Local Search Algorithms Using Easylocal++

    • Luca Di Gaspero, Andrea Schaerf
    Pages 155-175

  7. Integrating Heuristic Search and One-Way Constraints in the Iopt Toolkit

    • Christos Voudouris, Raphaël Dorne
    Pages 177-191

  8. The OptQuest Callable Library

    • Manuel Laguna, Rafael Martí
    Pages 193-218

  9. A Constraint Programming Toolkit for Local Search

    • Paul Shaw, Vincent Furnon, Bruno De Backer
    Pages 219-261

  10. The Modeling Language OPL — A Short Overview

    • Pascal Van Hentenryck, Laurent Michel
    Pages 263-294

  11. Genetic Algorithm Optimization Software Class Libraries

    • Andrew R. Pain, Colin R. Reeves
    Pages 295-329

  12. Back Matter

    Pages 331-360
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Keywords

About this book

Optimization problems in practice are diverse and evolve over time, giving rise to - quirements both for ready-to-use optimization software packages and for optimization software libraries, which provide more or less adaptable building blocks for app- cation-specific software systems. In order to apply optimization methods to a new type of problem, corresponding models and algorithms have to be “coded” so that they are accessible to a computer. One way to achieve this step is the use of a mod- ing language. Such modeling systems provide an excellent interface between models and solvers, but only for a limited range of model types (in some cases, for example, linear) due, in part, to limitations imposed by the solvers. Furthermore, while m- eling systems especially for heuristic search are an active research topic, it is still an open question as to whether such an approach may be generally successful. Modeling languages treat the solvers as a “black box” with numerous controls. Due to variations, for example, with respect to the pursued objective or specific problem properties, - dressing real-world problems often requires special purpose methods. Thus, we are faced with the difficulty of efficiently adapting and applying appropriate methods to these problems. Optimization software libraries are intended to make it relatively easy and cost effective to incorporate advanced planning methods in application-specific software systems. A general classification provides a distinction between callable packages, nume- cal libraries, and component libraries.

Editors and Affiliations

  • Braunschweig University of Technology, Germany

    Stefan Voß

  • University of California, Davis, USA

    David L. Woodruff

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