Implementing queue models with fuzzy and stochastic

Uncertainty and imprecision have a major impact on queue performance and decision-making in real-world systems, particularly in industrial, service, and computer contexts. The inherent fuzziness in real-world situations may be lost in traditional stochastic queue models since they rely on exact probability distributions. This research suggests a hybrid model that combines stochastic and fuzzy set theories to assess queue systems in the face of uncertainty, which would solve this shortcoming. In terms of factors like arrival rates, service periods, and system capacity, the method accounts for both language ambiguities and probabilistic variations. The model provides a more versatile and all-encompassing depiction of real-world queues by using fuzzy numbers in conjunction with traditional probabilistic distributions. To show how successful the suggested technique is in measuring performance and providing decision assistance, it is applied to different queue topologies, such as finite and infinite buffer systems. When compared to traditional models, the hybrid fuzzy-stochastic model offers more robust results and deeper insights, making it a useful tool for analysts and system designers working with imperfect or missing data..