Optimal Buffer and Server Allocation in Markovian Queueing Networks by Particle Swarm Algorithm

Autores

Palavras-chave:

Queueing Theory, Servers, Buffers, Heuristic Optimization, Particle Swarm Optimization

Resumo

This study optimizes resource allocation in queueing network systems to improve operational efficiency using appropriate algorithms. Specifically, Particle Swarm Optimization (PSO) was applied to the buffer and server allocation problem (BSAP) in queueing networks with Markovian arrivals and service times, multiple servers, and finite buffers. In this context, the buffer and server allocation problem (BSAP) stands out, whose solution methodology can be applied to various real-world situations modeled as queues or queueing networks, such as manufacturing line systems, healthcare services, traffic models, among others. BSAP is computationally challenging as a nonlinear programming problem without a closed-form analytical solution, necessitating derivative-free methods like PSO. The PSO algorithm is considered a promising tool for finding efficient solutions, thus enabling improved resource management. The study examines the algorithm’s ability to deliver cost-effective and suitable solutions that accommodate variations in relative costs between servers and buffers, as well as the specific characteristics of each network topology.

Biografia do Autor

Marina Campos Oliveira, Universidade Federal de Ouro Preto

Marina Campos Oliveira holds a Bachelor's degree in Statistics from the Federal University of Ouro Preto (2025), where he conducted research in applied statistics and stochastic process. Ms. Oliveira has experience in queue theory and applied statistics. His primary research interests include data analysis, artificial intelligence, and optimization.

Josino José Barbosa, Universidade Federal de Ouro Preto

Josino José Barbosa holds a Bachelor's degree in Statistics from the Federal University of Ouro Preto (2014), where he conducted research in applied statistics and multivariate statistics. He earned a Master's degree in Applied Statistics and Biometrics from the Federal University of Viçosa (2017), with a focus on multivariate outlier detection. He subsequently obtained a Ph.D. in Applied Statistics and Biometrics from the Federal University of Viçosa (2020), specializing in statistics, probability, and computational statistics. Professor Barbosa has extensive experience in multivariate statistics, outlier detection, and applied statistics. His primary research interests include data analysis, applied probability, and optimization. He has authored several research articles published in both national and international peer-reviewed journals and conference proceedings.

Helgem de Souza Ribeiro Martins, Universidade Federal de Ouro Preto

Helgem de Souza Ribeiro Martins holds a Bachelor's degree in Statistics from the Federal University of Ouro Preto (2014), where he conducted research in applied statistics and computational simulation. He earned a Master's degree in Statistics from the Federal University of Minas Gerais (2016), with a focus on stochastic processes and queueing theory. He subsequently obtained a Ph.D. in Applied Statistics and Biometrics from the Federal University of Viçosa (2020), specializing in statistics, probability, and computational statistics. Professor Martins has extensive experience in data science, data visualization, and applied statistics. His primary research interests include data analysis, data visualization, and optimization. He has authored several research articles published in both national and international peer-reviewed journals and conference proceedings.

Gabriel Lima de Souza, Universidade Federal de Ouro Preto

Gabriel Lima de Souza holds a bachelor's degree in Statistics from the Federal University of Ouro Preto (UFOP) (2014), a master's degree in Computer Science from UFOP (2020), and a PhD in Computer Science from UOP (2025). Professor Souza has experience in Probability and Statistics, with an emphasis on Applied Statistics and Optimization, working mainly on the following topics: Queuing theory, Optimization, Conflicting Objectives, and Heuristic Optimization. He is the author of research studies published in national and international journals and conference proceedings.

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Publicado

26-12-2025

Como Citar

Campos Oliveira, M., José Barbosa, J., de Souza Ribeiro Martins, H., Lima de Souza, G., & Ribeiro Duarte, A. (2025). Optimal Buffer and Server Allocation in Markovian Queueing Networks by Particle Swarm Algorithm. Sigmae, 14(5), 01–17. Recuperado de https://publicacoes.unifal-mg.edu.br/revistas/index.php/sigmae/article/view/2736

Edição

v. 14 n. 5 (2025): Edição temática: XII Workshop em Estatística Aplicada e Biometria da UFV

Seção

Estatística Aplicada

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