Study on single server finite capacity neutrosophic queueing model

• Published in: Computational & applied mathematics 2025-02, Vol.44 (1), Article 5
• Main Authors: Dayana, K., Poongodi, T., Vennila, B.
• Format: Article
• Language: English
• Subjects: Applications of Mathematics; Computational Mathematics and Numerical Analysis; Decision making; Finite capacity; Mathematical Applications in Computer Science; Mathematical Applications in the Physical Sciences; Mathematics; Mathematics and Statistics; Performance evaluation; Performance measurement; Probability distribution functions; Queuing theory; Statistical analysis
• ISSN: 2238-3603; 1807-0302
• DOI: 10.1007/s40314-024-02878-1

The research paper offers a comprehensive evaluation of the performance measures, compared to traditional ones that consider only the deterministic or probabilistic outcomes. Performance measures derived from neutrosophic queueing models can be used for optimization and decision-making purposes. By quantifying the degree of truth, indeterminacy, and falsity in performance metrics, decision-makers can make informed decisions to improve queueing system efficiency and effectiveness. Research in this area is relatively new and ongoing. Compared to other neutrosophic representations, SVTNNs are relatively easier to understand and implement due to their well-defined trapezoidal shape. In this paper a neutrosophic method is used to calculate new performance in Markovian model with a single server and finite capacity. The symbol for it is NM/NM/1/When expressed in terms of a single-valued trapezoidal neutrosophic number (SVTNN), the arrival rate and the service rate are both represented, enabling a more comprehensive analysis of system performance. This model has an exponential distribution for service time and a Poisson process for arrival rates. In this case, the neutrosophic queueing model is transformed into a crisp model by means of Zadeh's extension principle and the ( α , β , γ )-cut technique, the neutrosophic queueing model is converted to a deterministic model. To further comprehend the analytical process presented in this research, an illustrated example is provided.