2004/11/01 by Garud Iyengar, Karl Sigman · 1 citation
Business, Management and Accounting · Decision Sciences · Mathematics · #Advanced Queuing Theory Analysis #Simulation Techniques and Applications #Supply Chain and Inventory Management #math.PR #msc:90C59 #msc:93E03 #msc:93E35
paper · pdf · doi:10.1214/105051604000000936
published as Annals of Applied Probability 2004, Vol. 14, No. 4, 1698-1740 · Published at http://dx.doi.org/10.1214/105051604000000936 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2004/11/01 · arxiv created 2005/03/24 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/29
We introduce penalty-function-based admission control policies to approximately maximize the expected reward rate in a loss network. These control policies are easy to implement and perform well both in the transient period as well as in steady state. A major advantage of the penalty approach is that it avoids solving the associated dynamic program. However, a disadvantage of this approach is that it requires the capacity requested by individual requests to be sufficiently small compared to total available capacity. We first solve a related deterministic linear program (LP) and then translate an optimal solution of the LP into an admission control policy for the loss network via an exponential penalty function. We show that the penalty policy is a target-tracking policy—it performs well because the optimal solution of the LP is a good target. We demonstrate that the penalty approach can be extended to track arbitrarily defined target sets. Results from preliminary simulation studies are included.