Deterministic Equivalents for Optimizing and Satisficing under Chance Constraints

A. Charnes and W. W. Cooper
Additional contact information
A. Charnes: Northwestern University, Evanston, Illinois
W. W. Cooper: Carnegie Institute of Technology, Pittsburgh, Pennsylvania

Operations Research, 1963, vol. 11, issue 1, 18-39

Abstract: Chance constrained programming admits random data variations and permits constraint violations up to specified probability limits. Different kinds of decision rules and optimizing objectives may be used so that, under certain conditions, a programming problem (not necessarily linear) can be achieved that is deterministic---in that all random elements have been eliminated. Existence of such “deterministic equivalents” in the form of specified convex programming problems is here established for a general class of linear decision rules under the following 3 classes of objectives (1) maximum expected value (“E model”), (2) minimum variance (“V model”), and (3) maximum probability (“P model”). Various explanations and interpretations of these results are supplied along with other aspects of chance constrained programming. For example, the “P model” is interpreted so that H. A. Simon's suggestions for “satisficing” can be studied relative to more traditional optimizing objectives associated with “E” and “V model” variants.

Date: 1963
References: Add references at CitEc
Citations: View citations in EconPapers (103)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.11.1.18 (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:oropre:v:11:y:1963:i:1:p:18-39

Access Statistics for this article

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