Queueing Systems with Leadtime Constraints: A Fluid-Model Approach for Admission and Sequencing Control

European Journal of Operational Research 167 (2005) pp. 179-207

29 Pages Posted: 8 Feb 2013

Date Written: May 18, 2004


We study how multi-product queueing systems should be controlled so that sojourn times (or end-to-end delays) do not exceed specified leadtimes. The network dynamically decides when to admit new arrivals and how to sequence the jobs in the system. To analyze this difficult problem, we propose an approach based on fluid model analysis that translates the leadtime specifications into deterministic constraints on the queue length vector. The main benefit of this approach is that it is possible (and relatively easy) to construct scheduling and multi-product admission policies for leadtime control. Additional results are: (a) While this approach is simpler than a heavy-traffic approach, the admission policies that emerge from it are also more specific than, but consistent with, those from heavy-traffic analysis, (b) A simulation study gives a first indication that the policies also perform well in stochastic systems, (c) Our approach specifies a "tailored" admission region for any given sequencing policy. Such joint admission and sequencing control is "robust" in the following sense: system performance is relatively insensitive to the particular choice of sequencing rule when used in conjunction with tailored admission control. As an example, we discuss the tailored admission regions for two well-known sequencing policies: Generalized Processor Sharing and Generalized Longest Queue. (d) While we first focus on the multi-product single server system, we do extend to networks and identify some subtleties.

Keywords: queueing, scheduling, lead times, admission control, fluid models

Suggested Citation

Maglaras, Costis and Van Mieghem, Jan Albert, Queueing Systems with Leadtime Constraints: A Fluid-Model Approach for Admission and Sequencing Control (May 18, 2004). European Journal of Operational Research 167 (2005) pp. 179-207, Available at SSRN: https://ssrn.com/abstract=2213304

Costis Maglaras

Columbia University - Columbia Business School, Decision Risk and Operations ( email )

New York, NY
United States

Jan Albert Van Mieghem (Contact Author)

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics