CanQueue 2026

Information Design in Queueing Systems: From Fluid Limits to Human Limits

We study the use of delay information in queueing systems from two perspectives: a macro system-level view and a micro behavioral view. Our approach utilizes a diverse methodological toolkit, ranging from heavy-traffic limits to controlled behavioral experiments with human subjects. By bridging these methodologies, we highlight robust insights that emerge across different modeling techniques. Specifically, we show that more information is not always better and that vague or binary signaling can paradoxically enhance performance. Finally, we address the gap between rigorous mathematical modeling and the nuances of human decision-making, offering a way forward to integrate both into a unified, behaviorally-aware framework for operational design.

This talk is based on two papers. The complete list of my co-authors is as follows: Philipp Afeche, Junqi Hu, Vahid Sarhangian, Arturo Estrada, and Mirko Kremer.

Rouba Ibrahim is Professor and Head of the Operations and Technology group at the School of Management, University College London (UCL). Her research focuses on stochastic modelling applications to service systems, especially call centers and healthcare systems. In particular, she is interested in applying queueing-theoretic methods and numerical tools to problems relating to the operational management of service systems. She serves on the editorial boards of Management Science, Operations Research, and Queueing Systems. Rouba earned her PhD in Operations Research from Columbia University.

Improving the Judiciary: A Queueing Approach

Justice systems are high-stakes, complex operations that shape societies and lives through the rule of law. In this talk, I will discuss open problems where our community is well suited to tackle issues that can improve access, timeliness, and fairness of justice.

To illustrate the potential of such work, I will present my recent work on improving case adjudication delays in the Supreme Court of India. In this work, we model the court system as a two-stage case-management queueing system, and use data-driven calibrated simulations to surface the underlying system inefficiencies. We then propose policy interventions that can successfully tackle judicial delay, reducing delays by 65%.

Ramandeep Randhawa is the Charles L. and Ramona I. Hilliard Professor of Business Administration and Professor of Data Sciences and Operations at the USC Marshall School of Business. He is a leading scholar in queueing theory, service operations, and AI-enabled decision systems. He earned his PhD from Stanford University. His research has received multiple distinctions, including the 2025 MSOM Service SIG Best Paper Award for applying queueing theory to court system capacity and delay. Professor Randhawa currently serves as Area Editor for Operations Research.