@article{henmeytad03,
	abstract = {This paper concerns modelling and policy synthesis for regulation of multiclass queueing networks.
	
A 2-parameter network model is introduced to allow independent modelling of variability and mean processing-rates, while maintaining simplicity of the model. Policy synthesis is based on consideration of more tractable workload models, and then translating a policy from this abstraction to the discrete network of interest.

Translation is made possible through the use of safety-stocks that maintain feasibility of workload trajectories. This is a well-known approach in the queueing theory literature, and may be viewed as a generic approach to avoid deadlock in a discrete-event dynamical system.

Simulation is used to evaluate a given policy, and to tune safety-stock levels. These simulations are accelerated through a variance reduction technique that incorporates stochastic approximation to tune the variance reduction. The search for appropriate safety-stock levels is coordinated through a cutting plane algorithm.},
	author = {Henderson, S.~G. and Meyn, S. P. and Tadi\'{c}, V.},
	date-added = {2016-01-10 16:07:54 +0000},
	date-modified = {2016-01-10 16:07:54 +0000},
	journal = {Discrete Event Dynamic Systems},
	note = {Special issue on learning and optimization methods},
	pages = {149-189},
	title = {Performance Evaluation and Policy Selection in Multiclass Networks},
	url_paper = {pubs/DEDSV4.pdf},
	volume = {13},
	year = {2003}}

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Translation is made possible through the use of safety-stocks that maintain feasibility of workload trajectories. This is a well-known approach in the queueing theory literature, and may be viewed as a generic approach to avoid deadlock in a discrete-event dynamical system. Simulation is used to evaluate a given policy, and to tune safety-stock levels. These simulations are accelerated through a variance reduction technique that incorporates stochastic approximation to tune the variance reduction. The search for appropriate safety-stock levels is coordinated through a cutting plane algorithm.","author":[{"propositions":[],"lastnames":["Henderson"],"firstnames":["S. G."],"suffixes":[]},{"propositions":[],"lastnames":["Meyn"],"firstnames":["S.","P."],"suffixes":[]},{"propositions":[],"lastnames":["Tadić"],"firstnames":["V."],"suffixes":[]}],"date-added":"2016-01-10 16:07:54 +0000","date-modified":"2016-01-10 16:07:54 +0000","journal":"Discrete Event Dynamic Systems","note":"Special issue on learning and optimization methods","pages":"149-189","title":"Performance Evaluation and Policy Selection in Multiclass Networks","url_paper":"pubs/DEDSV4.pdf","volume":"13","year":"2003","bibtex":"@article{henmeytad03,\n\tabstract = {This paper concerns modelling and policy synthesis for regulation of multiclass queueing networks.\n\t\nA 2-parameter network model is introduced to allow independent modelling of variability and mean processing-rates, while maintaining simplicity of the model. Policy synthesis is based on consideration of more tractable workload models, and then translating a policy from this abstraction to the discrete network of interest.\n\nTranslation is made possible through the use of safety-stocks that maintain feasibility of workload trajectories. This is a well-known approach in the queueing theory literature, and may be viewed as a generic approach to avoid deadlock in a discrete-event dynamical system.\n\nSimulation is used to evaluate a given policy, and to tune safety-stock levels. These simulations are accelerated through a variance reduction technique that incorporates stochastic approximation to tune the variance reduction. The search for appropriate safety-stock levels is coordinated through a cutting plane algorithm.},\n\tauthor = {Henderson, S.~G. and Meyn, S. P. and Tadi\\'{c}, V.},\n\tdate-added = {2016-01-10 16:07:54 +0000},\n\tdate-modified = {2016-01-10 16:07:54 +0000},\n\tjournal = {Discrete Event Dynamic Systems},\n\tnote = {Special issue on learning and optimization methods},\n\tpages = {149-189},\n\ttitle = {Performance Evaluation and Policy Selection in Multiclass Networks},\n\turl_paper = {pubs/DEDSV4.pdf},\n\tvolume = {13},\n\tyear = {2003}}\n\n","author_short":["Henderson, S.","Meyn, S. 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