Queueing Causal Models: Comparative Analytics in Queueing Systems

References

• Asanjarani A, Nazarathy Y, Taylor P (2021) A survey of parameter and state estimation in queues. Queueing Systems 97:39–80.Crossref, Google Scholar
• Baron O (2021) Business analytics in service operations—Lessons from healthcare operations. Nav. Res. Logist. 68(5):517–533.Crossref, Google Scholar
• Baron O, Krass D, Senderovich A, Sherzer E (2024) Supervised ML for solving the GI/GI/1 queue. INFORMS J. Comput. 36(3):766–786.Link, Google Scholar
• Benkeser D, Van Der Laan M (2016) The highly adaptive lasso estimator. 2016 IEEE Internat. Conf. Data Sci. Adv. Anal. DSAA (IEEE, Piscataway, NJ), 689–696.Google Scholar
• Buzacott J, Shanthikumar J (1993) Stochastic Models of Manufacturing Systems, Prentice-Hall International Series in Industrial and Systems Engineering (Prentice-Hall, Saddle River, NJ).Google Scholar
• Camargo M, Dumas M, González-Rojas O (2020) Automated discovery of business process simulation models from event logs. Decision Support Systems 134:113284.Crossref, Google Scholar
• Carter MW, Blake JT (2005) Using simulation in an acute-care hospital: Easier said than done. Operations Research and Health Care: A Handbook of Methods and Applications (Springer, Boston), 191–215.Crossref, Google Scholar
• Corlu CG, Akcay A, Xie W (2020) Stochastic simulation under input uncertainty: A review. Oper. Res. Perspect. 7:100162.Crossref, Google Scholar
• Cox DR (1955) The statistical analysis of congestion. J. Roy. Statist. Soc. Ser. A Gen. 118(3):324–335.Crossref, Google Scholar
• Cruz FR, Santos MA, Oliveira F, Quinino R (2021) Estimation in a general bulk-arrival Markovian multi-server finite queue. Oper. Res. 21(1):73–89.Crossref, Google Scholar
• Deo S, Jain A (2019) Slow first, fast later: Temporal speed-up in service episodes of finite duration. Production Oper. Management 28(5):1061–1081.Crossref, Google Scholar
• Dieker AB, Hackman ST (2025) QPLEX: A Computational Modeling and Analysis Methodology for Stochastic Systems (Springer Nature, Cham, Switzerland).Google Scholar
• Elalouf A, Wachtel G (2021) Queueing problems in emergency departments: A review of practical approaches and research methodologies. Oper. Res. Forum 3:2.Crossref, Google Scholar
• Granger CW (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3):424–438.Crossref, Google Scholar
• Kim SHH (2014) Data-Driven Decisions in Service Systems (Columbia University, New York).Google Scholar
• Kohavi R, Rothleder NJ, Simoudis E (2002) Emerging trends in business analytics. Commun. ACM 45(8):45–48.Crossref, Google Scholar
• Krivulin NK (1995) A max-algebra approach to modeling and simulation of tandem queueing systems. Math. Comput. Model. 22(3):25–37.Crossref, Google Scholar
• Krivulin NK (2012) Max-plus algebra models of queueing networks. Preprint, submitted December 3, https://arxiv.org/abs/1212.0578.Google Scholar
• Kyritsis AI, Deriaz M (2019) A machine learning approach to waiting time prediction in queueing scenarios. 2019 Second Internat. Conf. Artificial Intelligence Indust. AI4I (IEEE, Piscataway, NJ), 17–21.Google Scholar
• Lindley DV (1952) The theory of queues with a single server. Math. Proc. Cambridge Philosophical Soc., vol. 48 (Cambridge University Press, Cambridge, UK), 277–289.Google Scholar
• Mittal D, Zheng S, Dong J, Namkoong H (2025) Data-driven stochastic modeling using autoregressive sequence models: Translating event tables to queueing dynamics. Preprint, submitted September 6, https://arxiv.org/abs/2509.05839.Google Scholar
• Nii S, Okudal T, Wakita T (2020) A performance evaluation of queueing systems by machine learning. 2020 IEEE Internat. Conf. Consumer Electronics Taiwan ICCE-Taiwan (IEEE, Piscataway, NJ), 1–2.Google Scholar
• Palomo S, Pender J (2020) Learning Lindley’s recursion. 2020 Winter Simul. Conf. (IEEE, Piscataway, NJ), 644–655.Google Scholar
• Palomo S, Pender J (2021) Learning the tandem network Lindley recursion. 2021 Winter Simul. Conf. (IEEE, Piscataway, NJ), 1–12.Google Scholar
• Papamakarios G, Nalisnick E, Rezende DJ, Mohamed S, Lakshminarayanan B (2021) Normalizing flows for probabilistic modeling and inference. J. Machine Learn. Res. 22(1):2617–2680.Google Scholar
• Pearl J (2009) Causality (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Peters J, Janzing D, Schölkopf B (2017) Elements of Causal Inference: Foundations and Learning Algorithms (MIT Press, Cambridge, MA).Google Scholar
• Robins J (1986) A new approach to causal inference in mortality studies with a sustained exposure period—Application to control of the healthy worker survivor effect. Math. Model. 7(9–12):1393–1512.Crossref, Google Scholar
• Senderovich A, Weidlich M, Yedidsion L, Gal A, Mandelbaum A, Kadish S, Bunnell CA (2016) Conformance checking and performance improvement in scheduled processes: A queueing-network perspective. Inform. Systems 62:185–206.Crossref, Google Scholar
• Sherzer E, Baron O, Krass D, Resheff Y (2025) Approximating G(t)/GI/1 queues with deep learning. Eur. J. Oper. Res. 322(3):889–907.Crossref, Google Scholar
• Shortle JF, Thompson JM, Gross D, Harris CM (2018) Fundamentals of Queueing Theory, vol. 399 (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
• Tank A, Covert I, Foti N, Shojaie A, Fox EB (2022) Neural granger causality. IEEE Trans. Pattern Anal. Machine Intelligence 44(8):4267–4279.Google Scholar
• Van der Laan MJ, Rose S (2011) Targeted Learning: Causal Inference for Observational and Experimental Data, vol. 4 (Springer, New York).Crossref, Google Scholar
• Van der Laan MJ, Rose S (2018) Targeted Learning in Data Science (Springer, Cham, Switzerland).Crossref, Google Scholar
• Whitt W (1983) The queueing network analyzer. Bell System Tech. J. 62(9):2779–2815.Crossref, Google Scholar
• Whitt W (1989) An interpolation approximation for the mean workload in a GI/G/1 queue. Oper. Res. 37(6):936–952.Link, Google Scholar
• Whitt W (1993) Approximations for the GI/G/M queue. Production Oper. Management 2(2):114–161.Crossref, Google Scholar
• Whitt W, You W (2022) A robust queueing network analyzer based on indices of dispersion. Nav. Res. Logist. 69(1):36–56.Crossref, Google Scholar