Waiting-Time Prediction with Invisible Customers

43 Pages Posted: 25 Oct 2021 Last revised: 6 Nov 2021

See all articles by Yoav Kerner

Yoav Kerner

Ben-Gurion University of the Negev

Ricky Roet-Green

University of Rochester - Simon Business School

Arik Senderovich

University of Toronto

Yaron Shaposhnik

University of Rochester - Simon Business School

Yuting Yuan

University of Rochester - Simon Business School

Date Written: October 20, 2021

Abstract

Problem definition: We study the problem of predicting in real time customers' expected waiting time in partially visible service systems. Unlike previous research, in our problem, the predictor only observes a subset of the customers who interact with the system, while all customers are served indiscriminately.

Methodology/results: We apply a theoretical analysis to a novel partially visible parsimonious queueing model that highlights the technical challenges in obtaining an analytical solution in more general systems. Subsequently, we use the solution as an element of a data-driven approach to solve the problem in more general settings. We also conduct extensive numerical experiments using the simulated data of queueing models inspired by the related literature and real data from a large outpatient hospital. The experiments suggest that the approach is robust to a large extent, yet it is also limited when critical data are missing or when the underlying theoretical model is "too far" from the actual system.

Managerial implications: Our work shows that standard methods fail and instead provides practical tools to predict waiting times in partially visible service systems, as well as insights into how much observable data are actually needed to make sufficiently accurate predictions in partially visible queueing systems.

Keywords: waiting time prediction, queues, invisible customers, machine learning.

Suggested Citation

Kerner, Yoav and Roet-Green, Ricky and Senderovich, Arik and Shaposhnik, Yaron and Yuan, Yuting, Waiting-Time Prediction with Invisible Customers (October 20, 2021). Available at SSRN: https://ssrn.com/abstract=3946696 or http://dx.doi.org/10.2139/ssrn.3946696

Yoav Kerner

Ben-Gurion University of the Negev ( email )

1 Ben-Gurion Blvd
Beer-Sheba 84105, 84105
Israel

Ricky Roet-Green

University of Rochester - Simon Business School ( email )

Rochester, NY 14627
United States

Arik Senderovich

University of Toronto ( email )

Toronto, Ontario M5S 3G8
Canada

Yaron Shaposhnik

University of Rochester - Simon Business School ( email )

Rochester, NY 14627
United States

Yuting Yuan (Contact Author)

University of Rochester - Simon Business School ( email )

Rochester, NY 14627
United States

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