Notes on linear programming-part XLVIII: inequalities for stochastic linear programming problems.

Albert Madansky

ResearchPublished 1958

A consideration of a linear-programming problem in which the "right-hand side" is a random vector whose expected value is known and where the expected value of the objective function is minimized. The conditions are studied under which an approximate solution (found by replacing the "right-hand side" by its expected value and solving the resulting linear programming problem) is satisfactory. In particular, conditions are given for the equality of the expected value of the objective function for the optimal solution and the value of the objective function for the approximate solution. Bounds on these values are also given. The relation is discussed between this problem and a related problem where an observation is made on the "right-hand side" and where the non- stochastic linear programming problem based on this observation is solved

Order a Print Copy

Format
Paperback
Page count
19 pages
List Price
$20.00
Buy link
Add to Cart

Topics

Document Details

  • Copyright: RAND Corporation
  • Availability: Available
  • Year: 1958
  • Print Format: Paperback
  • Paperback Pages: 19
  • Paperback Price: $20.00
  • Document Number: RM-2287

Citation

RAND Style Manual
Madansky, Albert, Notes on linear programming-part XLVIII: inequalities for stochastic linear programming problems. RAND Corporation, RM-2287, 1958. As of September 23, 2024: https://www.rand.org/pubs/research_memoranda/RM2287.html
Chicago Manual of Style
Madansky, Albert, Notes on linear programming-part XLVIII: inequalities for stochastic linear programming problems. Santa Monica, CA: RAND Corporation, 1958. https://www.rand.org/pubs/research_memoranda/RM2287.html. Also available in print form.
BibTeX RIS

This publication is part of the RAND research memorandum series. The research memorandum series, a product of RAND from 1948 to 1973, included working papers meant to report current results of RAND research to appropriate audiences.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.