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Synchronous resource allocation: modeling, capacity, and optimization

Author

Listed:
  • Sigrún Andradóttir

    (Georgia Institute of Technology)

  • Hayriye Ayhan

    (Georgia Institute of Technology)

  • Douglas G. Down

    (McMaster University)

Abstract

We explore settings where it is necessary (due to physical or operational constraints) or desirable (due to synergies or ease of implementation) to assign resources to tasks in a synchronous manner. We model the system as a queueing network with flexible servers and introduce the notion of a configuration to address the synchronous assignment of servers. This allows for a unified approach to determine the effects of resource synchronization, covering a wide range of problems in the literature. The maximal capacity of the system is given by the solution of a linear programming problem that also provides the optimal fractions of time the servers should spend in different configurations. This is used as a basis for constructing policies that have capacity arbitrarily close to the maximal capacity. We contrast synchronous server assignment with an asynchronous approach (focusing on independently scheduling individual servers rather than configurations) and show that synchronous server assignment is attractive with respect to applicability (it can capture constraints on server assignment and synergies among servers), implementation (it may have significantly fewer combinations of server allocations), and capacity (when both are applicable, asynchronous and synchronous server assignment will yield the same maximal capacity). Finally, we illustrate our modeling framework using several examples.

Suggested Citation

  • Sigrún Andradóttir & Hayriye Ayhan & Douglas G. Down, 2022. "Synchronous resource allocation: modeling, capacity, and optimization," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1287-1310, December.
    • Handle: RePEc:spr:orspec:v:44:y:2022:i:4:d:10.1007_s00291-022-00684-x
    DOI: 10.1007/s00291-022-00684-x
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    References listed on IDEAS

    as
    1. Down, Douglas G. & Karakostas, George, 2008. "Maximizing throughput in queueing networks with limited flexibility," European Journal of Operational Research, Elsevier, vol. 187(1), pages 98-112, May.
    2. Jaakko Peltokorpi & Henri Tokola & Esko Niemi, 2015. "Worker coordination policies in parallel station systems: performance models for a set of jobs and for continuous arrival of jobs," International Journal of Production Research, Taylor & Francis Journals, vol. 53(6), pages 1625-1641, March.
    3. Alexandre Mas & Enrico Moretti, 2009. "Peers at Work," American Economic Review, American Economic Association, vol. 99(1), pages 112-145, March.
    4. Sigrún Andradóttir & Hayriye Ayhan & Douglas G. Down, 2011. "TECHNICAL NOTE---Queueing Systems with Synergistic Servers," Operations Research, INFORMS, vol. 59(3), pages 772-780, June.
    5. Sigrún Andradóttir & Hayriye Ayhan & Douglas G. Down, 2007. "Compensating for Failures with Flexible Servers," Operations Research, INFORMS, vol. 55(4), pages 753-768, August.
    6. Itay Gurvich & Ward Whitt, 2009. "Scheduling Flexible Servers with Convex Delay Costs in Many-Server Service Systems," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 237-253, June.
    7. Sigrún Andradóttir & Hayriye Ayhan & Douglas G. Down, 2003. "Dynamic Server Allocation for Queueing Networks with Flexible Servers," Operations Research, INFORMS, vol. 51(6), pages 952-968, December.
    8. Hyun-Soo Ahn & Mark E. Lewis, 2013. "Flexible Server Allocation and Customer Routing Policies for Two Parallel Queues When Service Rates Are Not Additive," Operations Research, INFORMS, vol. 61(2), pages 344-358, April.
    9. Suri Gurumurthi & Saif Benjaafar, 2004. "Modeling and analysis of flexible queueing systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(5), pages 755-782, August.
    10. J. G. Dai & Wuqin Lin, 2005. "Maximum Pressure Policies in Stochastic Processing Networks," Operations Research, INFORMS, vol. 53(2), pages 197-218, April.
    11. Kenneth L. Schultz & Tobias Schoenherr & David Nembhard, 2010. "An Example and a Proposal Concerning the Correlation of Worker Processing Times in Parallel Tasks," Management Science, INFORMS, vol. 56(1), pages 176-191, January.
    12. Masha Shunko & Julie Niederhoff & Yaroslav Rosokha, 2018. "Humans Are Not Machines: The Behavioral Impact of Queueing Design on Service Time," Management Science, INFORMS, vol. 64(1), pages 453-473, January.
    13. William C. Jordan & Stephen C. Graves, 1995. "Principles on the Benefits of Manufacturing Process Flexibility," Management Science, INFORMS, vol. 41(4), pages 577-594, April.
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