题　目：Queueing Analytics: Machine Learning, Causal Queueing, and SiMLQ for Data Driven Simulation

嘉　宾：Opher Baron，特聘教授，多伦多大学；徐正航，博士生，多伦多大学

主持人：花成 副教授 tyc1286太阳成集团

时　间：2024年9月11日（周三）10:00-11:30am

地　点：安泰楼B207室

内容简介：

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 processes that can be modeled as queueing systems. 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 causal queueing models, 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 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) 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 the DS approach would be effective for practical setting- where even experts QTs cannot provide closed-form results. We will finish with a short demo of SiMLQ. 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.

演讲人简介：

Opher Baron is a 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 also served as the Academic Director, MMA Program (2021-2023), and the Operations management and statistics area coordinator (2015-2021). 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 launched the Covidppehelp.ca platform with his colleagues. This platform has facilitated the flow of millions of PPE items to end-user customers during the global Covid19 pandemic. In 2024 he cofounded SiMLQ Inc. see WWW.SiMLQ.com. SiMLQ automatically constructs data-driven process Simulators (digital twins) by leveraging event log data, Machine Learning and Queueing theory. His research interests include queueing, business analytics, service operations (such as healthcare), autonomous vehicles, and revenue 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.

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