Queueing Theory vs. Machine Learning

Our paper “Delay Prediction for Managing Multi-Class Service Systems: An Investigation of Queueing Theory and Machine Learning Approaches” was accepted to IEEE Transactions on Engineering Management (impact factor: 8.7).
Read it to know when to prefer the simple queueing theory formulas over CPU-hungry time-intensive machine learning models, and when the latter models are worthwhile.
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Professor Paul Feigin and the talented Elisheva Chocron (MSc.) are my co-authors.

Read the abstract and continue to the full paper:

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 non-stationarity, non-exponential 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. Read more…