Bayesian modeling of abandonments in ticket queues. (13th December 2018)

• Record Type: Journal Article
• Authors: Kuzu, Kaan; Soyer, Refik
• Is Part Of: Naval research logistics. Volume 65:Number 6/7(2018)
• Page Start: 499
• Page End: 521
• Publication Date: 2018-12-13
• Subjects: Bayesian nonparametric -- Bayesian queues -- interval censored data -- Markov chain Monte Carlo -- ticket queue; Logistics, Naval -- Periodicals; Supplies and stores -- Periodicals; 359.07
• DOI: 10.1002/nav.21816
• Languages: English
• ISSNs: 0894-069X

Abstract

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.