Robust Queueing for Single-Server Queues with Abandonment

TL;DR

This paper introduces Robust Queueing approximations for single-server queues with customer abandonment, providing accurate, scalable steady-state performance estimates in complex, non-Markovian settings.

Contribution

It develops a novel RQ framework that approximates the virtual waiting time in GI/GI/1+GI queues, extending analysis beyond traditional Markovian assumptions.

Findings

Accurate steady-state waiting time approximations across various load conditions.

Robustness of the method compared to existing heavy-traffic benchmarks.

Extension capability to queues in series with upstream and downstream approximations.

Abstract

Single-server queues with customer abandonment arise in call centers and many service systems, but steady-state performance measures remain analytically intractable beyond Markovian assumptions. This paper develops Robust Queueing (RQ) approximations for the mean steady-state virtual waiting time (offered waiting time) in the GI/GI/1+GI model. The approach starts from a reverse-time supremum representation of the virtual waiting time as the reflection of an effective net-input process that accounts for abandonments. We approximate effective net-input increments by their mean plus a robustness parameter times their standard deviation. For the drift, we introduce a Poisson-surrogate compensator and show that the associated correction term is asymptotically negligible in the long-patience regime. For variability, we propose two implementable surrogates: (i) a deterministic time-change approximation that yields a first RQ algorithm, and (ii) a refined algorithm based on a heavy-traffic limit that produces a scale-dependent variance function capturing the variance-reduction effect of abandonment. The resulting steady-state approximation reduces to a one-dimensional fixed point solvable by bisection and takes as input the arrival index of dispersion for counts (IDC), the service-time squared coefficient of variation, and the patience-time distribution. We further show how to extend the method to queues in series by feeding an approximation of the upstream departure IDC into the downstream RQ algorithm. Extensive numerical experiments demonstrate that the refined RQ approximation is accurate across underload, critical loading, and overload, and remains robust relative to existing heavy-traffic and hazard-rate-scaling benchmarks.