Post-processing improvements in multi-objective optimization of general single-server finite queueing networks.
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Date
2023
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Abstract
An 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.
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Keywords
Conflicting objectives, Buffer allocation, Particle swarm optimization
Citation
SOUZA, 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.