Queueing Causal Models: Comparative Analytics in Queueing Systems

• 成果类型: Article
• 署名作者: Baron, Opher; Krass, Dmitry; Senderovich, Arik; van der Laan, Mark; Xu, Zhenghang
• 署名单位: University of Toronto; York University - Canada; University of California System; University of California Berkeley
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2024.1515
• 发表日期: 2026
• 关键词: simulation

摘要

Problem definition: Much of the focus of queueing theory (QT) is on performance evaluation that supports comparative analytics-that is, comparing performance measures under different interventions. However, closed-form queueing models are very sensitive to assumptions. We develop a data-driven Structural Causal Queueing Model (SCQM)-a form of structural causal models that automatically adapts to the data-generating process of queueing systems, finds causal relations, and supports comparative analytics. Numerical experiments show that the accuracy of SCQM is competitive with QT, even for examples where analytical queueing solutions are available. Methodology: We employ structural causal modeling methodology that uses queueing-relevant features to develop a simulator that replicates the system's data-generating process without requiring prior knowledge of its dynamics. We apply Machine Learning models for identifying the parent sets and causal relations. We then provide intervention analysis using Monte Carlo simulation. Managerial implications: We use queueing knowledge to develop an accurate self-adapting data-driven performance evaluator for congested systems that requires no prior knowledge of the system dynamics. Using this method, companies can perform comparative analytics of interventions for queueing systems that may not be analytically solvable.