Supervised ML for Solving the GI/GI/1 Queue
References
- (2018) The history began from AlexNet: A comprehensive survey on deep learning approaches. Preprint, submitted March 3, https://arxiv.org/abs/1803.01164.Google Scholar
- (2003) Applied Probability and Queues (Springer, New York).Google Scholar
- (2006) Dynamic control of an m/m/1 service system with adjustable arrival and service rates. Management Sci. 52(11):1778–1791.Link, Google Scholar
- (2023) Supervised ML for solving the GI/GI/1 queue. https://dx.doi.org/10.1287/ijoc.2022.0263.cd, https://github.com/INFORMSJoC/2022.0263.Google Scholar
- (2022) Can machines solve general queueing problems? 2022 Winter Simulation Conf. (WSC) (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 2830–2841.Google Scholar
- (2005) Matching three moments with minimal acyclic phase type distributions. Stochastic Models 21(2–3):303–326.Crossref, Google Scholar
- (2011) Applications of machine learning approach on multi-queue message scheduling. Expert Systems Appl. 38(4):3323–3335.Crossref, Google Scholar
- (2015) A recurrent latent variable model for sequential data. Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R, eds. NIPS’15: Proc. 28th Internat. Conf. Neural Inform. Processing Systems, vol. 2 (MIT Press, Cambridge, MA).Google Scholar
- (2022) Queueing network controls via deep reinforcement learning. Stochastic Systems 12(1):30–67.Link, Google Scholar
- (2021) Estimation of the optimal threshold policy in a queue with heterogeneous servers using a heuristic solution and artificial neural networks. Mathematics 9(11):2227–7390.Crossref, Google Scholar
- (2001) Dynamic control of a queue with adjustable service rate. Oper. Res. 49(5):720–731.Link, Google Scholar
- (2021) Multivariate goodness-of-fit tests based on Wasserstein distance. Electronic J. Statist. 15(1):1328–1371.Crossref, Google Scholar
- (2013) Performance Modeling and Design of Computer Systems: Queueing Theory in Action (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2021) Predicting patient waiting time in the queue system using deep learning algorithms in the emergency room. Internat. J. Indust. Engrg. Oper. Management 3(1):33–45.Google Scholar
- (2007) Matching more than three moments with acyclic phase type distributions. Stochastic Models 23(2):167–194.Crossref, Google Scholar
- (2009) On the canonical representation of phase type distributions. Performance Evaluation 66(8):396–409.Crossref, Google Scholar
- (2019) Markovian Performance Evaluation with BuTools (Springer, New York), 253–268.Crossref, Google Scholar
- (2012) Efficient generation of ph-distributed random variates. Al-Begain K, Fiems D, Vincent JM, eds. Analytical and Stochastic Modeling Techniques and Applications (Springer, Berlin, Heidelberg), 271–285.Crossref, Google Scholar
- (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. Preprint, submitted February 24, https://arxiv.org/abs/1602.07360.Google Scholar
- (1962) On queues in heavy traffic. J. Royal Statist. Soc. Ser. B Methodological 24(2):383–392.Crossref, Google Scholar
- (1977) Queueing systems, volume 2: Computer applications. Networks 7(3):285–286.Google Scholar
- (2019) A machine learning approach to waiting time prediction in queueing scenarios. 2019 Second Internat. Conf. Artificial Intelligence Indust. (AI4I) (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 17–21.Google Scholar
- (2020) Deep-learning-based wireless resource allocation with application to vehicular networks. Proc. IEEE 108(2):341–356.Crossref, Google Scholar
- (2019) Reinforcement learning for optimal control of queueing systems. 2019 57th Annual Allerton Conf. Comm. Control Comput. (Allerton) (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 663–670.Google Scholar
- (1981) Matrix-Geometric Solutions in Stochastic Models (Springer, Berlin).Google Scholar
- (2011) Dirichlet and Related Distributions: Theory, Methods and Applications (Wiley, West Sussex, UK).Crossref, Google Scholar
- (2020) A performance evaluation of queueing systems by machine learning. 2020 IEEE Internat. Conf. Consumer Electronics Taiwan (ICCE-Taiwan), Taoyuan, Taiwan (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 1–2.Google Scholar
- (2015) mapfit: An R-based tool for PH/MAP parameter estimation. Campos J, Haverkort BR, eds. Quantitative Evaluation of Systems (Springer International Publishing, Cham, Switzerland), 105–112.Crossref, Google Scholar
- (2021) A survey of the usages of deep learning for natural language processing. IEEE Trans. Neural Networks Learning Systems 32(2):604–624.Crossref, Google Scholar
- (2022) Algorithms for queueing systems with reneging and priorities modeled as quasi-birth-death processes. INFORMS J. Comput. 34(3):1693–1710.Link, Google Scholar
- (1986) An algorithm for PH/PH/c queues. Eur. J. Oper. Res. 23(1):118–127.Crossref, Google Scholar
- (2022) Supervised learning-based approximation method for single-server open queueing networks with correlated interarrival and service times. Internat. J. Production Res. 60(22):6822–6847.Crossref, Google Scholar
- (2007) A minimal representation of Markov arrival processes and a moments matching method. Performance Evaluation 64(9):1153–1168.Crossref, Google Scholar
- (2022) A data-driven approach to deriving closed-form approximations for queueing problems using genetic algorithms. Queueing Systems 100(3):549–551.Crossref, Google Scholar
- (2006) Quasi-birth-and-death processes with an explicit rate matrix. Stochastic Models 22(1):77–98.Crossref, Google Scholar
- (2018) Optimization of global production scheduling with deep reinforcement learning. Procedia CIRP 72:1264–1269.Crossref, Google Scholar
- (1983) The queueing network analyzer. Bell System Tech. J. 62(9):2779–2815.Crossref, Google Scholar
- (1993) Approximations for the GI/G/m queue. Production Oper. Management 2(2):114–161.Crossref, Google Scholar
- (2018) Using robust queueing to expose the impact of dependence in single-server queues. Oper. Res. 66(1):184–199.Link, Google Scholar
- (2019) The advantage of indices of dispersion in queueing approximations. Oper. Res. Lett. 47(2):99–104.Crossref, Google Scholar
- (2022) A robust queueing network analyzer based on indices of dispersion. Naval Res. Logist. 69(1):36–56.Crossref, Google Scholar
- (2019) A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources. Transportation Res. Part C Emerging Tech. 107:248–265.Google Scholar
- (2019) A robust queueing network analyzer based on indices of dispersion. Unpublished doctoral dissertation, Columbia University, New York, https://academiccommons.columbia.edu/doi/10.7916/d8-j4sj-cn69/download.Google Scholar
- (2018) Improved Adam optimizer for deep neural networks. 2018 IEEE/ACM 26th Internat. Sympos. Quality Service (IWQoS) (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 1–2.Google Scholar
- (2021) A comprehensive survey on transfer learning. Proc. IEEE 109(1):43–76.Crossref, Google Scholar

