SRPT Scheduling Discipline in Many-Server Queues with Impatient Customers

References

• Banerjee S, Budhiraja A, Puha AL (2020) Heavy traffic scaling limits for shortest remaining processing time queues with heavy tailed processing time distributions. Working paper, http://arxiv.org/pdf/2003.03655.pdf.Google Scholar
• Bassamboo A, Randhawa RS (2015) Scheduling homogeneous impatient customers. Management Sci. 62(7):2129–2147.Link, Google Scholar
• Batt RJ, Terwiesch C (2015) Waiting patiently: An empirical study of queue abandonment in an emergency department. Management Sci. 61(1):39–59.Link, Google Scholar
• Bramson M (1998) State space collapse with application to heavy traffic limits for multiclass queueing networks. Queueing Systems 30(1-2):89–140.Crossref, Google Scholar
• Brown L, Gans N, Mandelbaum A, Sakov A, Shen H, Zeltyn S, Zhao L (2005) Statistical analysis of a telephone call center: A queueing-science perspective. J. Amer. Statist. Assoc. 100(469):36–50.Crossref, Google Scholar
• Chen Y, Dong J (2020) Scheduling with service time information: The power of two priority classes. Working paper, https://arxiv.org/pdf/2105.10499.pdf.Google Scholar
• Dai J, He S (2013) Many-server queues with customer abandonment: Numerical analysis of their diffusion model. Stochastic Systems 3(1):96–146.Link, Google Scholar
• Dai J, Tezcan T (2011) State space collapse in many-server diffusion limits of parallel server systems. Math. Oper. Res. 36(2):271–320.Link, Google Scholar
• Down DG, Gromoll HC, Puha AL (2009) Fluid limits for shortest remaining processing time queues. Math. Oper. Res. 34(4):880–911.Link, Google Scholar
• Garnett O, Mandelbaum A, Reiman M (2002) Designing a call center with impatient customers. Manufacturing Service Oper. Management 4(3):208–227.Link, Google Scholar
• Gromoll HC, Kruk Ł, Puha AL (2011) Diffusion limits for shortest remaining processing time queues. Stochastic Systems 1(1):1–16.Link, Google Scholar
• Grosof I, Scully Z, Harchol-Balter M (2018) Srpt for multiserver systems. Performance Evaluation 127:154–175.Crossref, Google Scholar
• Kang W, Ramanan K (2010) Fluid limits of many-server queues with reneging. Ann. Appl. Probability 20(6):2204–2260.Crossref, Google Scholar
• Kim J, Ward AR (2013) Dynamic scheduling of a gi/gi/1+ gi queue with multiple customer classes. Queueing Systems 75(2-4):339–384.Crossref, Google Scholar
• Kim J, Randhawa RS, Ward AR (2018) Dynamic scheduling in a many-server, multiclass system: The role of customer impatience in large systems. Manufacturing Service Oper. Management 20(2):285–301.Link, Google Scholar
• Leonardi S, Raz D (2007) Approximating total flow time on parallel machines. J. Comput. System Sci. 73(6):875–891.Crossref, Google Scholar
• Lin M, Wierman A, Zwart B (2011) Heavy-traffic analysis of mean response time under shortest remaining processing time. Performance Evaluation 68(10):955–966.Crossref, Google Scholar
• Puha AL, Ward AR (2019) Scheduling an overloaded multiclass many-server queue with impatient customers. Operations Research & Management Science in the Age of Analytics. (INFORMS TutORials in Operations Research), 189–217.Link, Google Scholar
• Puha AL (2015) Diffusion limits for shortest remaining processing time queues under nonstandard spatial scaling. Ann. Appl. Probability 25(6):3381–3404.Crossref, Google Scholar
• Reiman MI (1984) Some diffusion approximations with state space collapse. Modelling and Performance Evaluation Methodology (Springer, New York), 207–240.Crossref, Google Scholar
• Schrage L (1968) Letter to the editor-a proof of the optimality of the shortest remaining processing time discipline. Oper. Res. 16(3):687–690.Link, Google Scholar
• Schrage LE, Miller LW (1966) The queue m/g/1 with the shortest remaining processing time discipline. Oper. Res. 14(4):670–684.Link, Google Scholar
• Scully Z, Harchol-Balter M, Scheller-Wolf A (2018) Soap: One clean analysis of all age-based scheduling policies. Proc. ACM on Measurement and Analysis of Comput. Systems 2, 1–30.Google Scholar
• Whitt W (2005) Engineering solution of a basic call-center model. Management Sci. 51(2):221–235.Link, Google Scholar
• Whitt W (2006) Fluid models for multiserver queues with abandonments. Oper. Res. 54(1):37–54.Link, Google Scholar