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SERVER COORDINATION IN QUEUEING SYSTEMS: WHEN AND HOW?

Published online by Cambridge University Press:  20 April 2021

Junqi Hu
Affiliation:
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0205, USA E-mail: sa@gatech.edu
Sigrún Andradóttir
Affiliation:
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0205, USA E-mail: sa@gatech.edu
Hayriye Ayhan
Affiliation:
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0205, USA E-mail: sa@gatech.edu

Abstract

Standard server assignment policies for multi-server queueing stations include the noncollaborative policy, where the servers work in parallel on different jobs; and the fully collaborative policy, where the servers work together on the same job. However, if each job can be decomposed into subtasks with no precedence relationships, then we consider a form of server coordination named task assignment where the servers work in parallel on different subtasks of the same job. We identify the task assignment policy that maximizes the long-run average throughput of a queueing station with finite internal buffers when blocked servers can be idled or reassigned to either replace or collaborate with other servers on unblocked subtasks. We then compare the server coordination policies and show that the task assignment is best when the servers are highly specialized; otherwise, the fully collaborative or noncollaborative policies are preferable depending on whether the synergy level among the servers is high or not. We also provide numerical results that quantify our previous comparison. Finally, we address buffer allocation in longer lines where there are precedence relationships between some of the tasks, and present numerical results that suggest our comparisons for one queueing station generalize to longer lines.

Type
Research Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

Ahn, H.S., Duenyas, I., & Lewis, M.E. (2002). Optimal control of a two-stage tandem queuing system with flexible servers. Probability in the Engineering and Informational Sciences 16(4): 453469.CrossRefGoogle Scholar
Andradóttir, S. & Ayhan, H. (2005). Throughput maximization for tandem lines with two stations and flexible servers. Operations Research 53(3): 516531.CrossRefGoogle Scholar
Andradóttir, S., Ayhan, H., & Down, D.G. (2001). Server assignment policies for maximizing the steady-state throughput of finite queueing systems. Management Science 47(10): 14211439.CrossRefGoogle Scholar
Andradóttir, S., Ayhan, H., & Down, D.G. (2003). Dynamic server allocation for queueing networks with flexible servers. Operations Research 51(6): 952968.CrossRefGoogle Scholar
Andradóttir, S., Ayhan, H., & Down, D.G. (2011). Queueing system with synergistic servers. Operations Research 59: 772780.CrossRefGoogle Scholar
Andradóttir, S., Ayhan, H., & Down, D.G. (2013). Optimal assignment of servers to tasks when collaboration is inefficient. Queueing Systems 75(1): 79110.CrossRefGoogle Scholar
Argon, N.T. & Andradóttir, S. (2006). Partial pooling in tandem lines with cooperation and blocking. Queueing Systems 52(1): 530.CrossRefGoogle Scholar
Buzacott, J.A. (1996). Commonalities in reengineered business processes: models and issues. Management Science 42(5): 768782.CrossRefGoogle Scholar
Hopp, W.J. & Oyen, M.P. (2004). Agile workforce evaluation: a framework for cross-training and coordination. IIE Transactions 36(10): 919940.CrossRefGoogle Scholar
Hordijk, A. & Koole, G. (1992). On the assignment of customers to parallel queues. Probability in the Engineering and Informational Sciences 6(4): 495511.CrossRefGoogle Scholar
Isik, T., Andradóttir, S., & Ayhan, H. (2016). Optimal control of queueing systems with non-collaborating servers. Queueing Systems 84(1–2): 79110.CrossRefGoogle Scholar
Ko, S. & Serfozo, R.F. (2004). Response times in M/M/s fork-join networks. Advances in Applied Probability, 36(3): 854871.CrossRefGoogle Scholar
Lebrecht, A. & Knottenbelt, W. (2007). Response time approximations in fork-join queues. In 23rd Annual UK Performance Engineering Workshop (UKPEW).Google Scholar
Tsai, Y.C. & Argon, N.T. (2008). Dynamic server assignment policies for assembly-type queues with flexible servers. Naval Research Logistics 55(3): 234251.CrossRefGoogle Scholar
Van Oyen, M.P., Gel, E.G., & Hopp, W.J. (2001). Performance opportunity for workforce agility in collaborative and noncollaborative work systems. IIE Transactions 33(9): 761777.CrossRefGoogle Scholar
Wang, X., Andradóttir, S., & Ayhan, H. (2015). Dynamic server assignment with task-dependent server synergy. IEEE Transactions on Automatic Control 60(2): 570575.CrossRefGoogle Scholar