Algorithms for Separable Nonlinear Resource Allocation Problems

Muralidharan S. Kodialam and Hanan Luss
Additional contact information
Muralidharan S. Kodialam: Bell Labs, Lucent Technologies, Holmdel, New Jersey
Hanan Luss: AT&T Labs, Holmdel, New Jersey

Operations Research, 1998, vol. 46, issue 2, 272-284

Abstract: We consider a simple resource allocation problem with a single resource constraint. The objective function is composed of separable, convex performance functions, one for each activity. Likewise, the constraint has separable, convex resource-usage functions, one for each activity. The objective is to minimize the sum of the performance functions, subject to satisfying the resource constraint and nonnegativity constraints. This problem extends the well-studied problem in which the resource constraint is linear. We present several algorithms to solve the problem. These algorithms extend approaches developed for the linearly constrained problem. They can readily solve large problems and find the optimal solution in a number of iterations that does not exceed the number of variables. We provide several examples for illustration purposes, present computational results, and highlight the similarities and differences among the algorithms.

Keywords: Programming; algorithms; allocation of one resource; Programming; nonlinear; resource allocation algorithms (search for similar items in EconPapers)
Date: 1998
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.46.2.272 (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:46:y:1998:i:2:p:272-284

Access Statistics for this article

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