Modeling and optimization of service systems under uncertainty: A fuzzy queueing approach with hybrid machine learning

Leila Mabrouka Zeghidi, Parmeet Kaur Chahal, Amina Angelika Bouchentouf and Kamlesh Kumar

Mathematics and Computers in Simulation (MATCOM), 2026, vol. 247, issue C, 545-573

Abstract: The inherent imprecision and variability prevalent in real-world complex operational systems, such as multi-component machining, often render traditional crisp queueing models inadequate for accurate performance analysis and optimization. This paper introduces a fuzzy FM/FM/c/M queueing model to address this limitation, integrating critical features like K-variant vacations, waiting servers, catastrophic events, and customer impatience (state-dependent balking and reneging). Central to our approach is the utilization of trapezoidal fuzzy numbers to represent crucial system parameters, thereby offering a more robust and precise representation of system dynamics. We utilize parametric nonlinear programming (P-NLP) coupled with α-cut analysis to derive interval-valued fuzzy performance metrics. Efficient solutions for these P-NLP problems, particularly for cost optimization, are obtained through the application of the hybrid random forest–particle swarm optimization (RF-PSO) method, and the standard PSO algorithm. Numerical illustrations demonstrate the model’s practical utility in visualizing parameter effects on performance indices through sensitivity analysis and solving the associated cost optimization problem.

Keywords: Queueing systems with catastrophes; K-variant vacations; Impatience; Fuzzy queues; Matrix analytic method; Hybrid random forest–particle swarm optimization (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:247:y:2026:i:c:p:545-573

DOI: 10.1016/j.matcom.2026.03.033

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