Forecasting anticipated incomes in the Markov networks with positive and negative customers

DOI10.1134/S0005117917050046zbMath1371.60163OpenAlexW2620187525MaRDI QIDQ2399744

M. A. Matalytskii

Publication date: 24 August 2017

Published in: Automation and Remote Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s0005117917050046

zbMATH Keywords

successive approximationsnegative customersMarkov queueing networkspositive customersanticipated income

Mathematics Subject Classification ID

Stochastic network models in operations research (90B15) Queueing theory (aspects of probability theory) (60K25) Queues and service in operations research (90B22) Markov renewal processes, semi-Markov processes (60K15) Applications of Markov renewal processes (reliability, queueing networks, etc.) (60K20)

Related Items (4)

FINDING EXPECTED REVENUES IN G-NETWORK WITH MULTIPLE CLASSES OF POSITIVE AND NEGATIVE CUSTOMERS ⋮ Queueing system with two input flows, preemptive priority, and stochastic dropping ⋮ Calculating expected incomes in open Markov networks with requests of different classes and different peculiarities ⋮ FINDING EXPECTED REVENUES IN G-NETWORK WITH SIGNALS AND CUSTOMERS BATCH REMOVAL

Cites Work

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• Analysis and forecasting of expected incomes in Markov networks with unreliable servicing systems
• Analysis and forecasting of expected incomes in Markov networks with bounded waiting time for the claims
• On some results in analysis and optimization of Markov networks with incomes and their applications
• \(G\)-networks: development of the theory of multiplicative networks
• A survey of Markov decision models for control of networks of queues
• Simulation modeling of HM-networks with consideration of positive and negative messages
• Product-form queueing networks with negative and positive customers
• Optimal Control of a Service Facility with Variable Exponential Service Times and Constant Arrival Rate

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