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Bayesian modeling of abandonments in ticket queues

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  • Kaan Kuzu
  • Refik Soyer

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.

Suggested Citation

  • Kaan Kuzu & Refik Soyer, 2018. "Bayesian modeling of abandonments in ticket queues," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(6-7), pages 499-521, September.
    • Handle: RePEc:wly:navres:v:65:y:2018:i:6-7:p:499-521
    DOI: 10.1002/nav.21816
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    References listed on IDEAS

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    Cited by:

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