Optimization and performance prediction of fault-tolerant repairable system in fuzzy environment with reboot, feedback, and retrial

Aditya Kulwant, Madhu Jain, Parmeet Kaur Chahal, Rakesh Kumar Meena and Arvind Kumar Singh

Mathematics and Computers in Simulation (MATCOM), 2025, vol. 238, issue C, 457-478

Abstract: This study investigates stochastic modeling of a repairable redundant fault-tolerant system with feedback policy, standby support, retrial, and reboot features. The steady-state queue size distributions are derived using a recursive approach. The queueing model is transformed from a crisp to a fuzzy environment through Zadeh’s extension principle and α-cut approach. Numerical simulations are conducted to analyze the system performance metrics with respect to diverse system parameters. The total expected cost of the fault-tolerant repairable system is established. Followed by sensitivity analysis and relative sensitivity analysis, we evaluate the system indices and their behavior corresponding to the system parameters. Furthermore, the cost is optimized using meta-heuristic approaches, namely Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). A practical illustration demonstrating the applicability of the proposed model, with a focus on the high-frequency trading systems in financial markets, is provided.

Keywords: Fault-tolerant repairable system; Retrial; Feedback; Fuzzy logic; Particle Swarm Optimization (PSO); Grey Wolf Optimization (GWO) (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:238:y:2025:i:c:p:457-478

DOI: 10.1016/j.matcom.2025.06.010

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