Abstract
Fuzzy queueing theory (FQT) is a powerful tool used to model queueing systems taking into account their natural imprecision. The traditional FQT model assumes a fixed number of servers with constant fuzzy arrival and service rates. It is quite common, however, to encounter situations where the number of servers, the arrival rate, and the service rate change with time. In those cases, the variability of these parameters can significantly impact the behavior of the queueing system and complicate its analysis. In this paper, we propose a simulation method to deal with these problems that leads to realistic estimations of the behavior of the queueing system. To demonstrate the applicability of the technique, we have employed it to solve a real-world problem: the prediction of the impact of electric vehicles on power networks. Electric vehicles have recently become a popular topic for research and development, mainly for their potential ability to reduce emissions and dependence on fossil fuels. Their demanding charging requirements can, nevertheless, significantly impact the performance of the power grid. In this paper, we propose modeling the recharging process as a fuzzy queueing problem. This approach is more realistic than previous treatments, while leading to accurate descriptions of the performance of the system.
Published Version
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