Abstract
In this work, we suggest an analytic technique with triangular fuzzy and triangular intuitionistic fuzzy numbers to compute the membership functions of considerable state-executing proportion in Erlang service models. The inter-entry rate, which is Poisson, and the admin (service) rate, which is Erlang, are both fuzzy-natured in this case, with FEk designating the Erlang probabilistic deviation with k exponentially phase. The numeric antecedents are shown to validate the model's plausibility, FM/FEk/1. A contextual inquiry is also carried out, comparing individual fuzzy figures. Intuitionistic fuzzy queueing models that are comprehensible are more categorical than fuzzy queueing models. Expanding the fuzzy queuing model to an intuitionistic fuzzy environment can boost the implementation of the queuing model. The purpose of this study is to assess the performance of a single server Erlang queuing model with infinite capacity using fuzzy queuing theory and intuitionistic fuzzy queuing theory. The fuzzy queuing theory model's performance evaluations are reported as a range of outcomes, but the intuitionistic fuzzy queuing theory model provides a myriad of values. In this context, the arrival and the service rate are both triangular and intuitionistic triangular fuzzy numbers. An assessment is made to find evaluation criteria using a design protocol in which fuzzy values are kept as they are and not made into crisp values, and two statistical problems are solved to understand the existence of the method.
Publisher
Pakistan Journal of Statistics and Operation Research
Subject
Management Science and Operations Research,Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability
Reference30 articles.
1. Ashok Kumar, V. (2011). A membership function solution approach to fuzzy queue with Erlang service model. International Journal of Mathematical Sciences and Applications, 1(2):881-891.
2. Atanassov, K. (1999). Intuitionistic Fuzzy set: Theory and Applications. Springer Physica-Verlag, Berlin.
3. Atanassov, K., and Gargor, G. (1989). Interval valued intuitionistic fuzzy set. Fuzzy Sets and Systems, 31(3): 343-349.
4. Barak, S., and Fallahnezhad, M. (2012). Cost Analysis of Fuzzy Queuing Systems. International Journal of Applied Operational Research, 2(2):25-36.
5. Buckley, J. (1990). Elementary queuing theory based on possibility theory. Fuzzy Sets and Systems, 37(1):43-52.