Sortie Allocation by a Nonlinear Programming Model for Determining a Munitions Mix

by R. J. Clasen, Glenn W. Graves, John Y. Lu

Download

Full Document

FormatFile SizeNotes
PDF file 1.7 MB

Use Adobe Acrobat Reader version 10 or higher for the best experience.

Purchase

Purchase Print Copy

 FormatList Price Price
Add to Cart Paperback80 pages $25.00 $20.00 20% Web Discount

Presents a mathematical programming approach to maximize a military objective function which is subject to resource constraints. The formulation takes account of the diminishing returns obtained with incremental capability increases, thus producing a problem of the convex programming type. A very general nonlinear programming algorithm is presented, along with a discussion of its numerical properties and a proof of its convergence for the convex programming problem. This is followed by a description of the computer input for the specific problem studied, and by an example problem with its associated computer output. An appendix details the use of the general algorithm for applications that differ from the one considered here. This usage involves altering several PL/1 procedures to the form desired.

This report is part of the RAND Corporation Report series. The report was a product of the RAND Corporation from 1948 to 1993 that represented the principal publication documenting and transmitting RAND's major research findings and final research.

Permission is given to duplicate this electronic document for personal use only, as long as it is unaltered and complete. Copies may not be duplicated for commercial purposes. Unauthorized posting of RAND PDFs to a non-RAND Web site is prohibited. RAND PDFs are protected under copyright law. For information on reprint and linking permissions, please visit the RAND Permissions page.

The RAND Corporation 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.