Can Machines Solve General Queueing Problems?

Opher Baron, Dmitry Krass, Eliran Sherzer, Arik Senderovich

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


We study how well a machine can solve a general problem in queueing theory, using a neural net to predict the stationary queue-length distribution of an M/G/1 queue. This problem is, arguably, the most general queuing problem for which an analytical 'ground truth' solution exists. We overcome two key challenges: (1) generating training data that provide 'diverse' service time distributions, and (2) providing continuous service distributions as input to the neural net. To overcome (1), we develop an algorithm to sample phase-type service time distributions that cover a broad space of non-negative distributions; exact solutions of M / PH /1 (with phase-type service) are used for the training data. For (2) we find that using only the first n moments of the service times as inputs is sufficient to train the neural net. Our empirical results indicate that neural nets can estimate the stationary behavior of the M/G/1 extremely accurately.

Original languageEnglish
Title of host publicationProceedings of the 2022 Winter Simulation Conference, WSC 2022
EditorsB. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9798350309713
StatePublished - 2022
Externally publishedYes
Event2022 Winter Simulation Conference, WSC 2022 - Guilin, China
Duration: 11 Dec 202214 Dec 2022

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2022 Winter Simulation Conference, WSC 2022


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