Expressions for expected incomes and variances in systems of HM (Howard-Matalytski)-queueing network are obtained. Queueing systems are unreliable, service channels in them are exposed to random failure. It is supposed, that service rate of messages, rate of work of serviceable channels and restoration rate of faulty channels depends on messages number in these systems. The case when incomes from transitions between network's states are random variables with the given moments of first two orders is thus considered.
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The article deals with the technique allowing to estimate and forecast expected incomes, logistics transport systems subjects warehouse squares. The technique is based on application of HM (Howard-Matalytski) - queueing networks with incomes.
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In article the stochastic model of local computer network (LCN) in queueing network (QN) with one-line systems which are functioning in condition of high load is investigated. Optimization problem of finding optimal channel capacity which minimizes customer's fee for communication service during given time interval is solved. For finding of average number of calls which serve and wait in server queues the method of multidimensional generating functions was used.
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In article method of finding of expected incomes in systems of HM-network of arbitrary topology when incomes from transitions between network states are stochastic variables with given mean values is proposed. For expected incomes the system of linear non-homogeneous ordinary differential equations was obtained, to solve it we can find incomes in network systems.
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Expressions for expected incomes and variations of incomes in systems of Markov HM-networks, when service rates of messages are depending or not depending of network's states are obtained. The case when incomes of transitions between network's states are random variables with the set moments of first two orders is considered. Solution of some optimization problems for HM-networks are resulted.
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Investigation of the open exponential queueing network of arbitrary architecture with incomes is carry out in paper. The incomes of transitions between network's states are random variables with specified moments of two first orders. The expressions for expected incomes and variances of incomes are received.
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The multivariate generating functions method for obtaining of the open queueing network state probabilities, functioning with heavy load conditions, with dependent on time parameters of input flow and servicing is considered in this paper.
This paper provides the analysis and applications of networks with multi-type messages of multiple classes in systems. The network state is a vector, which components represent the number of messages in queues of the system. We obtained the sufficient conditions of representing the stationary distribution of the process, describing these networks, in the product form of factors characterizing separate systems.
Investigation of incomes in Markov network with central system, which could be a model of incomes changing in banking network, bas been dane. Systems incomes erom passages between network' s states depend on time. Set of equations for incomes bas being solved by numerical method. Also asymptotic analysis of incomes on large time period bas being dane.
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In the present paper the analysis of models for claim processing in insurance companies when the total number of insurance contracts may be a function of time is carried out. Closed by the structure queueing networks with bounded time of claims stay in the queues of processing systems serves as models for claim processing.
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In the present paper an analysis of models for eqitype and multi-type claims processing, when general number of insurance contracts is function of time, is carried out. The closed queueing networks are the models for claims processing. The problem of optimal number of estimators on definite intervals of time is considered.
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This paper provides the method of generating functions using for calculating the time-dependent state probabilities for open queuing networks in transient regime, when the network works in conditions of peak demand.
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