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On the Simultaneous Optimization of Server and Work Allocations in Production Line Systems with Variable Processing Times

Author

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  • Frederick S. Hillier

    (Stanford University, Stanford, California)

  • Kut C. So

    (University of California, Irvine, California)

Abstract

The allocation of servers and the allocation of work are two important decision variables in designing production line systems. Previous studies of each variable in isolation have found that throughput is maximized by using an allocation that gives preferential treatment to interior stations (especially center stations) over the two end stations. In this paper we study the simultaneous optimization of server and work allocations and obtain some surprising results of a different nature. One key finding is the L-phenomenon , whereby the throughput is maximized by assigning all extra servers over one per station to just one of the end stations and then adjusting the work allocation so that this station has by far the greatest amount of work per server. Another key finding is the multiple-server phenomenon , whereby extra servers add far more throughput per server than the initial one-per-station servers. Both findings have important implications for the design of some production line systems in ways that will greatly improve their efficiency. Similar conclusions are drawn when lower and/or upper bounds are imposed on the number of servers per station and the number of stations is included as a decision variable.

Suggested Citation

  • Frederick S. Hillier & Kut C. So, 1996. "On the Simultaneous Optimization of Server and Work Allocations in Production Line Systems with Variable Processing Times," Operations Research, INFORMS, vol. 44(3), pages 435-443, June.
    • Handle: RePEc:inm:oropre:v:44:y:1996:i:3:p:435-443
    DOI: 10.1287/opre.44.3.435
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    Citations

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    Cited by:

    1. Powell, Stephen G., 2000. "Specialization, teamwork, and production efficiency," International Journal of Production Economics, Elsevier, vol. 67(3), pages 205-218, October.
    2. 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.
    3. Sigrún Andradóttir & Hayriye Ayhan & Douglas G. Down, 2001. "Server Assignment Policies for Maximizing the Steady-State Throughput of Finite Queueing Systems," Management Science, INFORMS, vol. 47(10), pages 1421-1439, October.
    4. Xiuli Chao & Liming Liu & Shaohui Zheng, 2003. "Resource Allocation in Multisite Service Systems with Intersite Customer Flows," Management Science, INFORMS, vol. 49(12), pages 1739-1752, December.
    5. Liu, Liming & Yuan, Xue-Ming & Liu, John J., 2004. "Operational capacity allocation for unreliable module-based assembly systems," European Journal of Operational Research, Elsevier, vol. 155(1), pages 134-153, May.
    6. Refael Hassin & Yair Y. Shaki & Uri Yovel, 2015. "Optimal service‐capacity allocation in a loss system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(2), pages 81-97, March.
    7. Ramesh Arumugam & Maria Mayorga & Kevin Taaffe, 2009. "Inventory based allocation policies for flexible servers in serial systems," Annals of Operations Research, Springer, vol. 172(1), pages 1-23, November.
    8. 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.
    9. Nilay Tanık Argon & Sigrún Andradóttir, 2017. "Pooling in tandem queueing networks with non-collaborative servers," Queueing Systems: Theory and Applications, Springer, vol. 87(3), pages 345-377, December.

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