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Queueing Networks (queueing + network)

Distribution by Scientific Domains
Engineering66%

Selected Abstracts

Optimal Price and Quantity of Refurbished Products

PRODUCTION AND OPERATIONS MANAGEMENT, Issue 3 2006
Jumpol Vorasayan
Many retail product returns can be refurbished and resold, typically at a reduced price. The price set for the refurbished products affects the demands for both new and refurbished products, while the refurbishment and resale activities incur costs. To maximize profit, a manufacturer in a competitive market must carefully choose the proportion of returned products to refurbish and their sale price. We model the sale, return, refurbishment, and resale processes in an open queueing network and formulate a mathematical program to find the optimal price and proportion to refurbish. Examination of the optimality conditions reveals the different situations in which it is optimal to refurbish none, some, or all of the returned products. Refurbishing operations may increase profit or may be required to relieve a manufacturing capacity bottleneck. A numerical study identifies characteristics of the new product market and refurbished products that encourage refurbishing and some situations in which small changes in the refurbishing cost and quality provoke large changes in the optimal policy. [source]

Adaptive state- dependent importance sampling simulation of markovian queueing networks

EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS, Issue 4 2002
Pieter-Tjerk De Boer
In this paper, a method is presented for the efficient estimation of rare-event (buffer overflow) probabilities in queueing networks using importance sampling. Unlike previously proposed change of measures, the one used here is not static, i.e., it depends on the buffer contents at each of the network nodes. The ,optimal' state-dependent change of measure is determined adaptively during the simulation, using the cross-entropy method. The adaptive state-dependent importance sampling algorithm proposed in this paper yields asymptotically efficient simulation of models for which it is shown (formally or otherwise) that no effective static change of measure exists. Simulation results for queueing models of communication systems are presented to demonstrate the effectiveness of the method. [source]

On the application of forking nodes to product-form queueing networks,

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 2 2008
Essia H. Elhafsi
Abstract We define a ,forking node' as a service centre with one input feeding two outputs (each served by its own queue) under the control of an internal path-selection (PS) policy. We assume that both outputs lead to paths through which a packet reaches its final destination. However, the mean downstream delays on the two paths may be different and the PS policy should favour the path with the lower downstream delay. Using simulation, we compare the performance of this system under a variety of random, deterministic, state-dependent PS policies, including threshold-based and join-shortest-queue with bias (JSQ + b). We show that JSQ + b has better performance than the other alternatives. Moreover, if the input process to the forking node is Poisson, standard time series analysis techniques show that its two outputs are very close to being independent Poisson processes. Thus, if we find an accurate and efficient ,offline' analytical performance model for JSQ + b forking node, we can extend the applicability of product-form queueing networks to include such forking nodes. For this reason, we present several ways of modelling the performance of a JSQ + b node, using bounds, and compare their results on example networks. We establish a closed-form expression relating the bias b and the delays of the downstream paths. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Modelling congestion in closed queueing networks

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 4-5 2000
P.K. Pollett
Abstract We will consider the problem of identifying regions of congestion in closed queueing networks with state-dependent service rates. A particular queue will be called a bottleneck if the number of customers in that queue grows without bound as the total number of customers in the network becomes large. We will review methods for identifying potential bottlenecks, with a view to controlling congestion. We will see that the problem of identifying bottlenecks can be reduced to one of finding them in an isolated subnetwork with suitably modified routing intensities. Several special cases will be studied, illustrating a range of behaviour. For example, it is possible for a subnetwork to be congested, yet each queue in that subnetwork is not strictly a bottleneck. [source]