Queueing Analytics: Machine Learning, Causal Queueing, and SiMLQ for Data Driven Simulation
Opher Baron: Rotman School of Management, the University of Toronto, Hebrew University of Jerusalem, and SiMLQ Inc.}\\
Shany Azaria: Tel Aviv University
Dmitry Krass: Rotman School of Management, the University of Toronto and SiMLQ Inc.
Mark van der Laan: Department of Statistics, UC Berkley
Eliran Scherzer: University of Ariel
Arik Senderovich: York University and SiMLQ Inc.
Yehezkel Reshef: Tel Aviv University
Zhenghang Xu: Rotman School of Management, the University of Toronto
The objective of this talk is to expose researchers to the vast possibilities of using modern machinery and data for implementing effective management analytics for queueing processes. Such process are ubiquitous in modern economies, e.g., customers waiting to service, inventory waiting for processing/transportation, payments and invoices waiting to be generated/cleared, computing tasks waiting for resources. I will thus discuss recent developments in queueing analysis based on several papers.
We will start by defining management analytics along descriptive, predictive, comparative, i.e., comparing performance indicators under different interventions, and prescriptive analytics dimensions. We then shortly discuss ML solution for a G/G/1 based upon [1] and its extension to G(t)/G/1 based on [2].
Our main focus would be on structural causal queueing models (SCQM), based upon [3]. Not many organizations have queueing theorists (QTs) in their staff, but many organizations employ well trained Data Scientist (DS). Can DS use data and SCQM to provide accurate comparative analytics without expertise in queueing? We suggest a data-driven representation of system building blocks to create a non-queueing simulator without prior knowledge of the system. We show that this approach is effective in comparative analytics, when analyzing expected waits for an GI/M/1 with speed-ups. We first demonstrate that DS can successfully refine the parent sets of queueing variables from data using an off-the-shelf algorithm (even under a moderate sample size). We then use machine learning to estimate the causal structure in this queue, e.g., the Lindley's Recursion and use the G-computation to derive inference results of counterfactual interventions. For the GI/M/1 with speed-ups, we compare the performance of estimates obtained by a QT, who uses data driven estimates for the primitives of the queue, with those made by a DS that uses either parametric (where inter-arrival and service time distributions are known) or nonparametric (where both distributions are unknown) SCQM based estimators. We find that the errors of the DS that requires no knowledge of the system's dynamic and its features and these of the QT (which requires this knowledge) are comparable. Our results suggest that SCQM would be effective for practical setting- where even experts QTs cannot provide closed-form results.
We will finish with a short demo of SiMLQ, see www.simlq.com. SiMLQ software uses Machine Learning to automate the visualization, Simulation, and optimization of Queueing processes. SiMLQ automatically constructs data-driven simulation models from event-log data collected by common information systems and enables users to improve processes resource management, increase efficiency, reduce cost, and manage risks. SiMLQ- from data to action.
[1] Sherzer E., Baron O., Berman O., Krass D. (2025) Re-direction in Queueing Networks with Two Customer Types: the Inter-Departure Analysis, COR, Volume 173, January 2025, 106867
[2] Sherzer E., Baron O., Krass D. Reshef, H., (2025) Approximating G(t)/GI/1 queues with deep learning, European Journal of Operations Research, Volume 322, Issue 3, 1 May 2025, Pages 889-90) https://doi.org/10.1016/j.ejor.2024.12.030
[3] Baron, O., Krass, D., van der Laan, M., Senderovich, A,. Xu Z. Queueing Causal Models: Comparative Analytics in Service Systems. Forthcoming M&SOM.
Opher Baron
Opher Baron Opher is a University of Toronto Distinguished Professor of Operations Management at the Rotman School of Management, University of Toronto and a cofounder and CEO of SiMLQ. He was a visiting associate Professor at the Industrial engineering and Management faculty of Technion (2009/10) and a visiting Professor at the School of Information Management and Engineering, Shanghai University of Finance and Economics (2016/17). He is a Distinguished Fellow at the School of Business Administration at The Hebrew University of Jerusalem (2025/26). Opher served as the Academic Director, MMA Program (2021/23), and the Operations management and statistics area coordinator (2015/21). He has a PhD in Operations Management from the Sloan School of Management at the Massachusetts Institute of Technology, and an MBA and BSc in Industrial Engineering and Management from the Technion. On the teaching front, Opher is especially proud of the modeling and analytics courses he introduced and teaches at Rotman. On the application front he and his colleagues launched the Covidppehelp.ca platform that facilitated the flow of millions of PPE items to end-user customers during the global Covid19 pandemic. In 2024 he cofounded SiMLQ Inc. SiMLQ automatically constructs data-driven process Simulators (digital twins) by leveraging event log data, Machine Learning and Queueing theory. On the theory side, Opher’s research interests include queueing, business analytics, service operations (such as healthcare), autonomous vehicles, and supply chain management. Opher's work is published in leading journals such as Operations Research, and Manufacturing & Service Operations Management, and he has won several research and teaching awards and grants, including the 1000 Talents Plan Scholar from the Shanghai Municipal Government, 2017 and the Rotman 2023 Distinguished Scholarly Contribution Award. Opher is active in the operations research and operations management community. He has given numerous invited keynote lectures and seminars, chaired several conferences, clusters, and sessions, and is currently serving on the advisory board and editorial boards of several leading journals, including Operations Research, and Manufacturing & Service Operations Management.