Delay Prediction for Managing Multiclass Service Systems: An Investigation of Queueing Theory and Machine Learning Approaches
系统比较了排队论与机器学习在单队列多服务员系统中预测等待时间的效果，发现排队论在多数情况下预测精度相当或更优且计算成本更低，但机器学习在特定高优先级客户场景下表现更好。
Customer waiting time prediction is key to managing service systems. Predicting how long a customer will wait for service at the time of their arrival can provide important information to the customer and serve as a tool for the operations manager. Recent studies that suggested machine learning algorithms for waiting time prediction as an alternative to the standard queueing theory approaches investigated specific systems with mixed results regarding the superiority of a particular approach. We provide a systematic investigation of common violations of queueing theory assumptions on waiting time prediction in the context of single-queue many-server systems. These violations include nonstationarity, nonexponential service times, state-dependent service times, abandonments, and customers with different priorities. Using different machine learning models as well as queueing-theory-based methods, we seek to determine under what regimes machine learning prediction is to be preferred to queueing-theory-based predictors. Our results suggest that queueing theory models produce comparable and frequently better predictions versus machine learning algorithms at a much lower computational cost. Under other assumptions, such as high priority for a specific type of customer, machine learning predictions may outperform queueing theory predictions. Our results may guide the selection of a delay prediction approach for service systems.