A novel approach for optimization in a fuzzy finite capacity queuing model with system cost and expected degree of customer satisfaction

Abstract

From a wide variety of queuing models, the finite-capacity queuing models are the most commonly used, where arrival and service rates follow an exponential distribution. Based on two criteria of system cost and expected degree of customer satisfaction, the present study defines a new productivity rate index and evaluates the optimization of a queuing model with finite capacity. In queuing models, obviously, as the number of servers increases, the length of waiting lines decreases, the expected degree of customer satisfaction enhances, and obviously the system cost increases. This study deals with the mathematical relationships involved in the computations of these two criteria, and proposes a novel approach to determine an optimal number of servers by considering a decision-maker's priority and establishing a trade-off between criteria.

Optimization of M/M/s/N Queueing Model with Reneging in a Fuzzy Environment

“…Pardo and Fuente [16] analyzed the design of a fuzzy finite capacity queueing model based on the degree of customer satisfaction. Shahin et al [17] dealt with the optimization in a fuzzy finite capacity queueing system and they provided an alternative approach to determine the optimal number of servers by considering two criteria, including the level of customer satisfaction and the total cost in a queueing system. Cruz and Woensel [18] provided an overview of different modeling issues, the performance evaluation, and optimization behavior of the finite queueing models based on cycle time, work-in-process.…”