EconPapers    
Economics at your fingertips  
 

Narrowing the Search for Optimal Call-Admission Policies Via a Nonlinear Stochastic Knapsack Model

Marco Cello (), Giorgio Gnecco (), Mario Marchese () and Marcello Sanguineti ()
Additional contact information
Marco Cello: University of Genoa
Giorgio Gnecco: Institute for Advanced Studies (IMT)
Mario Marchese: University of Genoa
Marcello Sanguineti: University of Genoa

Journal of Optimization Theory and Applications, 2015, vol. 164, issue 3, No 6, 819-841

Abstract: Abstract Call admission control with two classes of users is investigated via a nonlinear stochastic knapsack model. The feasibility region represents the subset of the call space, where given constraints on the quality of service have to be satisfied. Admissible strategies are searched for within the class of coordinate-convex policies. Structural properties that the optimal policies belonging to such a class have to satisfy are derived. They are exploited to narrow the search for the optimal solution to the nonlinear stochastic knapsack problem that models call admission control. To illustrate the role played by these properties, the numbers of coordinate-convex policies by which they are satisfied are estimated. A graph-based algorithm to generate all such policies is presented.

Keywords: Stochastic knapsack; Nonlinear constraints; Call admission control; Coordinate-convex policies; Structural properties; 90B15; 90B18; 90C10; 90C27; 68M10 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10957-014-0570-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:joptap:v:164:y:2015:i:3:d:10.1007_s10957-014-0570-2

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-014-0570-2

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:joptap:v:164:y:2015:i:3:d:10.1007_s10957-014-0570-2