An Introductory Tutorial on Stochastic Linear Programming Models

Suvrajeet Sen and Julia L. Higle
Additional contact information
Suvrajeet Sen: Department of Systems and Industrial Engineering, The University of Arizona, Tucson, Arizona 85721
Julia L. Higle: Department of Systems and Industrial Engineering, The University of Arizona, Tucson, Arizona 85721

Interfaces, 1999, vol. 29, issue 2, 33-61

Abstract: Linear programming is a fundamental planning tool. It is often difficult to precisely estimate or forecast certain critical data elements of the linear program. In such cases, it is necessary to address the impact of uncertainty during the planning process. We discuss a variety of LP-based models that can be used for planning under uncertainty. In all cases, we begin with a deterministic LP model and show how it can be adapted to include the impact of uncertainty. We present models that range from simple recourse policies to more general two-stage and multistage SLP formulations. We also include a discussion of probabilistic constraints. We illustrate the various models using examples taken from the literature. The examples involve models developed for airline yield management, telecommunications, flood control, and production planning.

Keywords: programming; stochastic; tutorial (search for similar items in EconPapers)
Date: 1999
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33)

Downloads: (external link)
http://dx.doi.org/10.1287/inte.29.2.33 (application/pdf)

Related works:
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:inm:orinte:v:29:y:1999:i:2:p:33-61

Access Statistics for this article

More articles in Interfaces from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().