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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">inform</journal-id><journal-title-group><journal-title xml:lang="ru">Информатика</journal-title><trans-title-group xml:lang="en"><trans-title>Informatics</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1816-0301</issn><issn pub-type="epub">2617-6963</issn><publisher><publisher-name>UIIP NASB</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.37661/1816-0301-2025-22-2-48-62</article-id><article-id custom-type="elpub" pub-id-type="custom">inform-1356</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>МАТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>MATHEMATICAL MODELING</subject></subj-group></article-categories><title-group><article-title>Анализ полуоткрытой сети массового обслуживания с адаптацией скоростей обслуживания к скоростям поступления запросов</article-title><trans-title-group xml:lang="en"><trans-title>Analysis of a semi-open queueing network with adaptation of service rates to the rates of arriving requests</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Дудин</surname><given-names>А. Н.</given-names></name><name name-style="western" xml:lang="en"><surname>Dudin</surname><given-names>A. N.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Дудин Александр Николаевич, доктор физико-математических наук, профессор, заведующий НИЛ прикладного вероятностного анализа</p><p>пр. Независимости, 4, Минск, 220030</p></bio><bio xml:lang="en"><p>Alexander N. Dudin, D. Sc. (Phys.-Math.), Prof., Head of the Research Laboratory of Applied Probability Analysis</p><p>av. Nezavisimosti, 4, Minsk, 220030</p></bio><email xlink:type="simple">dudin@bsu.by</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Дудина</surname><given-names>О. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Dudina</surname><given-names>O. S.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Дудина Ольга Сергеевна, кандидат физико-математических наук, ведущий научный сотрудник НИЛ прикладного вероятностного анализа</p><p>пр. Независимости, 4, Минск, 220030</p></bio><bio xml:lang="en"><p>Olga S. Dudina, Ph. D. (Phys.-Math.), Leading Researcher of the Research Laboratory of Applied Probability Analysis</p><p>av. Nezavisimosti, 4, Minsk, 220030</p></bio><email xlink:type="simple">dudina@bsu.by</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Дудин</surname><given-names>С. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Dudin</surname><given-names>S. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Дудин Сергей Александрович, кандидат физико-математических наук, ведущий научный сотрудник НИЛ прикладного вероятностного анализа</p><p>пр. Независимости, 4, Минск, 220030</p></bio><bio xml:lang="en"><p>Sergei A. Dudin, Ph. D. (Phys.-Math.), Leading Researcher of the Research Laboratory of Applied Probability Analysis</p><p>av. Nezavisimosti, 4, Minsk, 220030</p></bio><email xlink:type="simple">dudins@bsu.by</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Белорусский государственный университет</institution></aff><aff xml:lang="en"><institution>Belarusian State University</institution></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>10</day><month>07</month><year>2025</year></pub-date><volume>22</volume><issue>2</issue><fpage>48</fpage><lpage>62</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Дудин А.Н., Дудина О.С., Дудин С.А., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Дудин А.Н., Дудина О.С., Дудин С.А.</copyright-holder><copyright-holder xml:lang="en">Dudin A.N., Dudina O.S., Dudin S.A.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://inf.grid.by/jour/article/view/1356">https://inf.grid.by/jour/article/view/1356</self-uri><abstract><p>Цели. Решается задача нахождения основных вероятностных характеристик функционирования полуоткрытой сети массового обслуживания, в которой интенсивность входного потока в узлы имеет несколько возможных уровней. При изменении уровня интенсивности потока возможно изменение скорости обслуживания запросов в узлах сети с целью оптимизации ее функционирования.Методы. Для решения используется аппарат многомерных цепей Маркова с непрерывным временем и специальной блочной структурой инфинитезимального генератора.Результаты. Получены алгоритмы и формулы для вычисления блоков генератора этой цепи, что позволяет вычислять ее инвариантное распределение вероятностей. Найдены формулы для вычисления основных характеристик производительности сети по известному инвариантному распределению вероятностей состояний цепи Маркова. Приведен численный пример, иллюстрирующий зависимость основных характеристик производительности сети от пропускной способности ее узлов. При выбранном экономическом критерии качества функционирования сети продемонстрирована возможность оптимизации перераспределения ресурсов сети между ее узлами при изменении уровня входного потока.Заключение. Полученные результаты могут быть использованы для оптимизации функционирования различных реальных объектов, описываемых полуоткрытыми сетями массового обслуживания, например телекоммуникационных и логистических систем, мобильных роботизированных систем хранения, за счет адаптации распределения ресурсов сети между ее узлами к изменяющейся скорости поступления запросов</p></abstract><trans-abstract xml:lang="en"><p>Objectives. The problem of computation of the main probabilistic characteristics of operation of a semi-open queueing network, in which the intensity of the input flow to the nodes has several possible levels, is considered. When the changing the level of flow intensity occurs, it is possible to change the rate of requests service in the nodes in order to optimize the network functioning.Methods. The solution is based on the apparatus of multidimensional Markov chains with continuous time and a special block structure of the infinitesimal generator.Results. The generator blocks of this chain are calculated using algorithms and formulas, which allows the invariant probability distribution to be determined. Formulas for calculating the main characteristics of network performance using the known invariant probability distribution of the states of the Markov chain are derived. A numerical example is provided to illustrate how the dependence of the main characteristics of network performance depend on the throughput of its nodes. Using the selected economic criterion for network performance quality, it is demonstrated that the redistribution of network resources between its nodes can be optimised with the change of the arrival flow level.Conclusion. The obtained results can be used to optimize the functioning of various real objects described by semi-open queueing networks, for example, telecommunication and logistic systems, mobile robotic fulfillment systems, by adapting the distribution of network resources between its nodes to the changing rate of incoming requests</p></trans-abstract><kwd-group xml:lang="ru"><kwd>полуоткрытая сеть массового обслуживания</kwd><kwd>изменение скорости поступления&#13;
запросов</kwd><kwd>анализ производительности</kwd><kwd>цепь Маркова</kwd><kwd>вероятность отказа</kwd></kwd-group><kwd-group xml:lang="en"><kwd>semi-open queueing network</kwd><kwd>request arrival rate variation</kwd><kwd>performance analysis</kwd><kwd>Markov chain</kwd><kwd>loss probability</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Roy D. Semi-open queuing networks: a review of stochastic models, solution methods and new research areas. International Journal of Production Research, 2016, vol. 54, no. 6, рр. 1735–1752.</mixed-citation><mixed-citation xml:lang="en">Roy D. Semi-open queuing networks: a review of stochastic models, solution methods and new research areas. 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