Bounds for probabilistic integer programming problems

Darinka Dentcheva, András Prékopa, Andrzej Ruszczyski

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

We consider stochastic integer programming problems with probabilistic constraints. The concept of p-efficient points of a probability distribution is used to derive various equivalent problem formulations. Next we introduce new methods for constructing lower and upper bounds for probabilistically constrained integer programs. We also show how limited information about the distribution can be used to construct such bounds. The concepts and methods are illustrated on an example of a vehicle routing problem.

Original languageEnglish
Pages (from-to)55-65
Number of pages11
JournalDiscrete Applied Mathematics
Volume124
Issue number1-3
DOIs
StatePublished - 15 Dec 2002

Keywords

  • Column generation
  • Integer programming
  • Probabilistic constraints
  • Stochastic programming

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