Stochastic programs with binary distributions: structural properties of scenario trees and algorithms

Vit Prochazka and Stein Wallace
Additional contact information
Vit Prochazka: NHH Norwegian School of Economics

Computational Management Science, 2018, vol. 15, issue 3, No 5, 397-410

Abstract: 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 partial order on the scenarios, leading to 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 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10287-018-0312-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: Stochastic programs with binary distributions: Structural properties of scenario trees and algorithms (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comgts:v:15:y:2018:i:3:d:10.1007_s10287-018-0312-2

Ordering information: This journal article can be ordered from
http://www.springer. ... ch/journal/10287/PS2

DOI: 10.1007/s10287-018-0312-2

Access Statistics for this article

Computational Management Science is currently edited by Ruediger Schultz

More articles in Computational Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().