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|a (JST)3690256
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|b ger
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|e rakwb
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|a eng
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|a 60K25
|2 MSC
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|a 90B22
|2 MSC
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|a Humes,, C.
|e verfasserin
|4 aut
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|a The Delay of Open Markovian Queueing Networks: Uniform Functional Bounds, Heavy Traffic Pole Multiplicities, and Stability
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|c 1997
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|a Text
|b txt
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|a Computermedien
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|a For open Markovian queueing networks, we study the functional dependence of the mean number in the system (and thus also the mean delay since it is proportional to it by Little's Theorem) on the arrival rate or load factor. We obtain linear programs (LPs) which provide bounds on the pole multiplicity M of the mean number in the system, and automatically obtain lower and upper bounds on the coefficients <tex-math>$\{C_{i}\}$</tex-math> of the expansion <tex-math>$\rho C_{M}/(1-\rho)^{M}+\rho C_{M-1}/(1-\rho)^{M-1}+\cdots +\rho C_{1}/(1-\rho)+\rho C_{0}$</tex-math>, where ρ is the load factor, which are valid for all ρ ∈ [0, 1). Our LPs can thus establish the stability of open networks for all arrival rates within capacity, while providing uniformly bounding functional expansions for the mean delay, valid for all arrival rates in the capacity region. The coefficients <tex-math>$\{C_{i}\}$</tex-math> can be optimized to provide the best bound at any desired value of the load factor, while still maintaining its validity for all ρ ∈ [0, 1). While the above LPs feature L(L + 1)(M + 1)/2 variables where L is the number of buffers in the network, for balanced systems we further provide a lower dimensional LP featuring just S(S + 1)/2 variables, where S is the number of stations in the network. This bound asymptotically dominates in heavy traffic a bound obtainable from the Pollaczek-Khintchine formula, and can capture interactions between multiple bottleneck stations in heavy traffic. We also provide an explicit upper bound for all scheduling policies in acyclic networks, and for the FBFS policy in open re-entrant lines.
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|a Copyright 1997 Institute for Operations Research and the Management Sciences
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|a Queueing networks
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|a Open networks
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|a Performance evaluation
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|a Scheduling policies
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|a Delay
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|a Stability
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|a Heavy traffic behavior
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|a Applied sciences
|x Engineering
|x Transportation
|x Traffic
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|a Business
|x Business economics
|x Commercial production
|x Production resources
|x Resource management
|x Time management
|x Scheduling
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|a Behavioral sciences
|x Psychology
|x Cognitive psychology
|x Decision theory
|x Operations research
|x Queuing theory
|x Queueing networks
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|a Mathematics
|x Pure mathematics
|x Algebra
|x Coefficients
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|a Applied sciences
|x Computer science
|x Computer programming
|x Mathematical programming
|x Linear programming
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|a Mathematics
|x Mathematical values
|x Mathematical constants
|x Growth constants
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|a Information science
|x Information resources
|x Research data sources
|x Research review studies
|x Technical reports
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|a Business
|x Industry
|x Industrial sectors
|x Service industries
|x Hospitality industries
|x Restaurant industry
|x Restaurants
|x Automats
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|a Mathematics
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|a Physical sciences
|x Physics
|x Mechanics
|x Classical mechanics
|x Kinematics
|x Load forces
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|a research-article
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1 |
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|a Ou, J.
|e verfasserin
|4 aut
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1 |
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|a Kumar, P. R.
|e verfasserin
|4 aut
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|i Enthalten in
|t Mathematics of Operations Research
|d Institute for Operations Research and the Management Sciences
|g 22(1997), 4, Seite 921-954
|w (DE-627)320435318
|w (DE-600)2004273-5
|x 15265471
|7 nnns
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|g volume:22
|g year:1997
|g number:4
|g pages:921-954
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|u https://www.jstor.org/stable/3690256
|3 Volltext
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|d 22
|j 1997
|e 4
|h 921-954
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