Chance-Constrained Linear Programming with Distribution-Free Constraints
Abstract
The report is concerned with methods of approximating the chance- constrained set S = (x such that/Pr(A x=or< B)=or< alpha) when the underlying distribution, F(.) of the random variate (A, B) is non-normal. The resulting sets are completely distribution-free in that no assumptions are made about the form of F(.) or any of its parameters. The concept employed is the distribution- free tolerance region. This is a sample based region containing 100 alpha percent of the population, at a confidence level, beta. The elements of the distribution-free sets satisfy the chance-constraint, Pr(Ax = or < B) =or< alpha with a confidence of at least beta. Furthermore, the sample size required to attain this level of confidence is readily available in tabular or graphical form. The superiority of the distribution-free approach over existing chance- constrained methods is demonstrated using simulated gamma variates.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1971
- Accession Number
- AD0736874
Entities
People
- Frederick M. Allen
- R. N. Braswell
Organizations
- University of Florida