Programming Under Probabilistic Constraint with Discrete Random Variable

  • András Prékopa
  • Béla Vizvári
  • Tamás Badics
Part of the Applied Optimization book series (APOP, volume 13)

Abstract

The most important static stochastic programming models, that can be formulated in connection with a linear programming problem, where some of the right-hand side values are random variables, are: the simple recourse model, the probabilistic constrained model and the combination of the two. In this paper we present algorithmic solution to the second and third models under the assumption that the random variables have a discrete joint distribution. The solution method uses the concept of a p-level efficient point (pLEP) intoduced by the first author (1990) and works in such a way that first all pLEP’s are enumerated, then a cutting plane method does the rest of the job.

Keywords

Probability Distribution Function Probabilistic Constraint Discrete Random Variable Dual Method Efficient Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • András Prékopa
    • 1
  • Béla Vizvári
    • 2
  • Tamás Badics
    • 1
  1. 1.RUTCOR, Rutgers Center for Operations ResearchRutgers UniversityNew BrunswickUSA
  2. 2.RUTCOR, Dept. of Operations ResearchEötvös Loránd UniversityBudapestHungary

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