Characteristics of optimal workload allocation for closed queueing networks
dc.contributor.author | Lee, Heungsoon Felix | en_US |
dc.contributor.author | Srinivasan, Mandyam M. | en_US |
dc.contributor.author | Yano, Candace Arai | en_US |
dc.date.accessioned | 2006-04-10T14:40:11Z | |
dc.date.available | 2006-04-10T14:40:11Z | |
dc.date.issued | 1991-07 | en_US |
dc.identifier.citation | Lee, Heungsoon Felix, Srinivasan, Mandyam M., Yano, Candace A. (1991/07)."Characteristics of optimal workload allocation for closed queueing networks." Performance Evaluation 12(4): 255-268. <http://hdl.handle.net/2027.42/29251> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6V13-48TDCRJ-24/2/37de9f348badbf0edbd1303ea32b0035 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/29251 | |
dc.description.abstract | We consider the problem of allocating a given workload among the stations in a multi-server product-form closed queueing network to maximize the throughput. We first investigate properties of the throughput function and prove that it is pseudoconcave for some special cases. Some other characteristics of the optimal workload and its physical interpretation are also provided. We then develop two computational procedures to find the optimum workload allocation under the assumption that the throughput function is pseudoconcave in general. The primary advantage of assuming pseudoconcavity is that, under this assumption, satisfaction of first order necessary conditions is sufficient for optimality. Computational experience with these algorithms provides additional support for the validity of this assumption. Finally, we generalize the solution procedure to accommodate bounds on the workloads at each station. | en_US |
dc.format.extent | 1006425 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | Characteristics of optimal workload allocation for closed queueing networks | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Industrial & Operations Engineering, University of Michigan, Ann Arbor, MI 48109-2117, USA | en_US |
dc.contributor.affiliationum | Department of Industrial & Operations Engineering, University of Michigan, Ann Arbor, MI 48109-2117, USA | en_US |
dc.contributor.affiliationother | Department of Industrial Engineering, Southern Illinois University, Edwardsville, IL 62026-1802, USA | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/29251/1/0000308.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0166-5316(91)90004-M | en_US |
dc.identifier.source | Performance Evaluation | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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