Abstract
Ticket queues arise often in public and private sector operations. The service providers in ticket queues have limited information on customer abandonments because abandonment time data is interval censored. The censored nature of data poses challenges in modeling and analysis of abandonments and in developing staffing policies. To alleviate such challenges, we present a Bayesian framework for analysis of abandonments in ticket queues. In doing so, we propose parametric and semi-parametric modulated Poisson process models to describe the abandonment behavior and develop their Bayesian analysis using Markov chain Monte Carlo methods. We implement our models on actual abandonment data from a bank’ s ticket queue and illustrate how the proposed Bayesian framework can be used to provide insights for operations managers in predicting abandonments and in developing server allocation policies.
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