EconPapers    
Economics at your fingertips  
 

A Chance Constrained Multiple Choice Programming Algorithm

Ronald D. Armstrong and Joseph L. Balintfy
Additional contact information
Ronald D. Armstrong: University of Texas, Austin, Texas
Joseph L. Balintfy: University of Massachusetts, Amherst, Massachusetts

Operations Research, 1975, vol. 23, issue 3, 494-510

Abstract: This paper considers multiple choice programming problems in which the elements of the activity matrix can be normally distributed random variables or random vectors. The truncated block enumeration method of multiple choice programming is described and used in the development of an algorithm to solve problems of this type. Deterministic inequalities computed from the means and variances are employed by the block pivoting algorithm to assure fast convergence to a (sub)optimal solution. The solution will satisfy each constraint with the required marginal probabilities, but a lower bound of the joint probabilities is also computed. As an option, problems can be solved when the lower bound of the joint probability that all the constraints are satisfied is specified alone.

Date: 1975
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.23.3.494 (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:23:y:1975:i:3:p:494-510

Access Statistics for this article

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

 
Page updated 2025-03-19
Handle: RePEc:inm:oropre:v:23:y:1975:i:3:p:494-510