An assessment of Fuzzy n-Policy queues with infinite capacity

This study examines the application of fuzzy n-policy queues with infinite capacity to optimize queuing systems under uncertain and variable conditions. Traditional queuing models often assume precise data, but real-world scenarios frequently involve ambiguity in parameters such as arrival rates, service rates, and system thresholds. The fuzzy n-policy model offers a more flexible approach by allowing these parameters to be represented as fuzzy variables, capturing the inherent uncertainty in complex queuing environments. By analyzing infinite capacity queues within this framework, the study evaluates system performance metrics like expected queue length, waiting time, and server utilization under different fuzzy set assumptions. The results demonstrate that the fuzzy nnn-policy enhances system responsiveness by adjusting service initiation thresholds based on the degree of uncertainty in arrival and service processes. These findings contribute to the broader field of stochastic and fuzzy queuing systems, providing practical insights for industries where demand variability and service uncertainty are prevalent, such as telecommunications, healthcare, and logistics.