Stochastic Programs with Binary Distributions: Structural Properties of Scenario Trees and Algorithms

16 Pages Posted: 31 Oct 2017

See all articles by Vit Prochazka

Vit Prochazka

NHH Norwegian School of Economics - Department of Business and Management Science

Stein W. Wallace

Norwegian School of Economics (NHH) - Department of Business and Management Science

Date Written: August 1, 2017

Abstract

Binary random variables often refer to such as customers that are present or not, roads that are open or not, machines that are operable or not. At the same time, stochastic programs often apply to situations where penalties are accumulated when demand is not met, travel times are too long, or profits too low. Typical for these situations is that the penalties imply a partition of the scenarios into two sets: Those that can result in penalties for some decisions, and those that never lead to penalties. We demonstrate how this observation can be used to efficiently calculate out-of-sample values, find good scenario trees and generally simplify calculations. Most of our observations apply to general integer random variables, and not just the 0/1 case.

Keywords: stochastic programming, scenarios, binary random variables, algorithms

JEL Classification: C44, C60, C61

Suggested Citation

Prochazka, Vit and Wallace, Stein W., Stochastic Programs with Binary Distributions: Structural Properties of Scenario Trees and Algorithms (August 1, 2017). NHH Dept. of Business and Management Science Discussion Paper No. 2017/12, Available at SSRN: https://ssrn.com/abstract=3061783 or http://dx.doi.org/10.2139/ssrn.3061783

Vit Prochazka (Contact Author)

NHH Norwegian School of Economics - Department of Business and Management Science ( email )

Helleveien 30
Bergen, NO-5045
Norway

Stein W. Wallace

Norwegian School of Economics (NHH) - Department of Business and Management Science ( email )

Helleveien 30
Bergen, NO-5045
Norway

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
31
Abstract Views
433
PlumX Metrics