QOA-RetrailNet: A Quail Optimization-Driven Framework for Geo/G/1 Retrial Queues with Impatient and Priority-Aware Customers

The Geo/G/1 retrial queue with customers who are impatient and aware of their priorities depicts a single-server system in discrete time where arrivals follow a geometric distribution. Customers who see that to server is busy may try again after a random amount of time or leave to site if they are impatient. Priority mechanisms make sure that consumers get better service, whereas low-priority ones could have to wait longer or lose more customers. This model includes real-world situations like call centres or cloud services that have retry logic, service-level agreements, and customers who are impatient. This research introduces an innovative optimisation framework utilising to Quail Optimisation Algorithm (QOA) to improve performance in discrete-time retrial queue systems incorporating repair and vacation methods. To suggested QOA-Optimized model is designed to mitigate performance degradation in conventional Geo/G/1 and fixed-parameter retrial queues, which frequently experience significant delays, low utilisation, and ineffective retrial management. To system is checked against important metrics such mean delay (W), system utilisation (ρ), blockage probability (B), server idle probability, retry frequency (RF), mean orbit size (E[QI]), and vacation/repair-induced delay. QOA-based optimisation automatically adjusts important parameters (r, θ, φ, λ) to keep to system stable and reduce traffic. To proposed model is better than to others, as shown by to simulation results. In particular, to QOA-Optimized system had a mean delay that was 5.1 units shorter than to classical model's mean delay of 9.3 units and to fixed-parameter model's mean delay of 17.8 units. System use went up to 0.93, which is higher than to benchmarks of 0.71 and 0.86. To model also cut to chance of blocking to 0.13, to chance of being idle to 0.07, and to delay in vacation to 1.2 units. To mean orbit size also went down to 3.17, and to retry frequency went down a lot to 3.2. These changes show that QOA is good at balancing system load, cutting down on resource waste, and making to whole queuing process more efficient. framework has a lot of promise for use in service-oriented contexts that need to work in real time.