Bayesian modeling of abandonments in ticket queues

Abstract

Ticket queues (TQs) issue tickets to customers upon arrival, and are often used in the public and private sectors. Abandonment data collected by TQs is interval censored, which makes predicting customer abandonments a challenging problem. In this paper, we build a Bayesian framework for predicting abandonment counts in TQs to assist managers in workforce planning. In doing so, we propose parametric and semiparametric modulated Poisson process models and develop their Bayesian analyses using Markov chain Monte Carlo methods. We implement our models using actual abandonment data from a bank's TQ, and illustrate how we can provide managerial insights related to abandonment counts and server allocation policies.

Bayesian analysis of Markov modulated queues with abandonment

“…A Bayesian analysis of the Erlang‐A models is considered in Reference 12. Recently, Reference 15 considered modeling of abandonment data in ticket queues which is interval censored. The authors proposed parametric and semiparametric Poisson process models and developed their Bayesian analyses using actual abandonment data from a ticket queue.…”