Modeling with metaconstraints and semantic typing of variables

Andre A. Cire, John N. Hooker, Tallys Yunes

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Recent research in hybrid optimization shows that a combination of technologies that exploits their complementary strengths can significantly speed up computation. The use of high-level metaconstraints in the problem formulation can achieve a substantial share of these computational gains by better communicating problem structure to the solver. During the solution process, however, metaconstraints give rise to reformulations or relaxations that introduce auxiliary variables, and some of the variables in one metaconstraint's reformulation may be functionally the same as or related to variables in another metaconstraint's reformulation. These relationships must be recognized to obtain a tight overall relaxation. We propose a modeling scheme based on semantic typing that systematically addresses this problem while providing simpler, self-documenting models. It organizes the model around predicates and declares variables by associating each with a predicate through a keyword that is analogous to a database query. We present a series of examples to illustrate this idea over a wide variety of applications.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalINFORMS Journal on Computing
Volume28
Issue number1
DOIs
StatePublished - Dec 1 2016

    Fingerprint

Keywords

  • Hybrid methods
  • Metaconstraints
  • Modeling
  • Semantics

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

Cite this