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Generalized Disjunctive Programming

In: Pyomo — Optimization Modeling in Python

Author

Listed:
  • William E. Hart

    (Sandia National Laboratories)

  • Carl D. Laird

    (Sandia National Laboratories)

  • Jean-Paul Watson

    (Sandia National Laboratories)

  • David L. Woodruff

    (University of California, Davis)

  • Gabriel A. Hackebeil

    (University of Michigan)

  • Bethany L. Nicholson

    (Sandia National Laboratories)

  • John D. Siirola

    (Sandia National Laboratories)

Abstract

This chapter documents how to express and solve Generalized Disjunctive Programs (GDPs). GDP models provide a structured approach for describing logical relationships in optimization models.We show how Pyomo blocks provide a natural base for representing disjuncts and forming disjunctions, and we how to solve GDP models through the use of automated problem transformations.

Suggested Citation

  • William E. Hart & Carl D. Laird & Jean-Paul Watson & David L. Woodruff & Gabriel A. Hackebeil & Bethany L. Nicholson & John D. Siirola, 2017. "Generalized Disjunctive Programming," Springer Optimization and Its Applications, in: Pyomo — Optimization Modeling in Python, edition 2, chapter 0, pages 157-164, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-58821-6_9
    DOI: 10.1007/978-3-319-58821-6_9
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