Genetic Algorithm Optimized Fuzzy N-Policy Queue Using α-Cut based Performance Analysis
DOI:
https://doi.org/10.64137/3108-2637/IJMAR-V2I1P102Keywords:
Fuzzy Queueing System, N-Policy, Α-Cut Method, Genetic Algorithm, M/M/1 Queue, Optimization, Performance MeasuresAbstract
In real-life queueing systems, many parameters such as arrival rate and service rate are often uncertain or imprecise. To handle such uncertainty, fuzzy set theory provides an effective framework for modelling queueing systems. In this paper, an N-policy M/M/1 queueing model is studied in a fuzzy environment where system parameters are represented as fuzzy numbers. The α-cut approach is employed to transform the fuzzy parameters into interval values, allowing the evaluation of system performance measures such as the expected queue length and waiting time. To obtain the optimal operating policy, a Genetic Algorithm (GA) is applied to determine the optimal threshold value of that minimizes the total system cost. The proposed method combines fuzzy modeling with evolutionary optimization to improve decision-making under uncertainty. Numerical illustrations and graphical analysis are provided to demonstrate the effectiveness of the proposed approach. The results show that the integration of α-cut based fuzzy analysis with Genetic Algorithm optimization provides a flexible and efficient method for determining optimal policies in uncertain queueing environments.
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