Souza, Gabriel Lima deDuarte, Anderson RibeiroMoreira, Gladston Juliano PratesCruz, Frederico Rodrigues Borges da2023-07-242023-07-242023SOUZA, G. L. de et al. Post-processing improvements in multi-objective optimization of general single-server finite queueing networks. IEEE Latin America Transactions, v. 21, n. 3, p. 381-388, mar. 2023. Disponível em: <https://latamt.ieeer9.org/index.php/transactions/article/view/7020>. Acesso em: 06 jul. 2023.1548-0992http://www.repositorio.ufop.br/jspui/handle/123456789/17043An alternative mathematical programming formulation is considered for a mixed-integer optimization problem in queueing networks. The sum of the blocking probabilities of a general service time, single server, and the finite, acyclic queueing network is minimized, and so are the total buffer sizes and the overall service rates. A multi-objective genetic algorithm (MOGA) and a particle swarm optimization (MOPSO) algorithm are combined to solve this difficult stochastic problem. The derived algorithm produces a set of efficient solutions for multiple objectives in the objective function. The implementation of the optimization algorithms is dependent on the generalized expansion method (GEM), a classical tool used to evaluate the performance of finite queueing networks. We carried out a set of computational experiments to attest to the efficacy and efficiency of the proposed approach. In addition, we present a comparative analysis of the solutions before and after post-processing. Insights obtained from the study of complex queue networks may assist the planning of these types of queueing networks.en-USrestritoConflicting objectivesBuffer allocationParticle swarm optimizationArtigo publicado em periodicohttps://latamt.ieeer9.org/index.php/transactions/article/view/7020