Original scientific paper
https://doi.org/10.17535/crorr.2024.0013
ANFIS computing and cost optimization of an M/M/c/M queue with feedback and balking customers under a hybrid hiatus policy
Aimen Dehmi
; University of Bejaia, Faculty of Exact Sciences, Applied Mathematics Laboratory, 06000 Bejaia, Algeria
Mohamed Boualem
; University of Bejaia, Faculty of Technology, Research Unit LaMOS, 06000 Bejaia, Algeria
*
Sami Kahla
; Research Center in Industrial Technologies, P.O. Box 64, 16014 Cheraga, Algeria
Louiza Berdjoudj
; University of Bejaia, Faculty of Exact Sciences, Research Unit LaMOS, 06000 Bejaia, Algeria
* Corresponding author.
Abstract
The present investigation studies a hybrid hiatus policy for a finite-space Markovian queue, incorporating realistic features such as Bernoulli feedback, multiple servers, and balking customers. A hybrid hiatus policy combines both a working hiatus and a complete hiatus. As soon as the system becomes empty, the servers switch to a working hiatus. During a working hiatus, the servers operate at a reduced service rate. Upon completion of the working hiatus and in the absence of waiting customers, the servers enter a complete hiatus. Once the complete hiatus period concludes, the servers resume normal operations and begin serving waiting customers. In the context of Bernoulli feedback, the dissatisfied customer can re-enter the system to receive another service. By utilizing the Markov recursive approach, we examined the steady-state probabilities of the system and queue sizes and other queueing indices, viz. Average queue length, average waiting time, throughput, etc. Using the Quasi-Newton method, a cost function is developed to determine the optimal values of the system’s decision variables. Furthermore, a soft computing approach based on an adaptive neuro-fuzzy inference system (ANFIS) is employed to validate the accuracy of the obtained results.
Keywords
ANFIS computing; feedback multi-server queue; hybrid hiatus policy; optimization; recursive approach
Hrčak ID:
321258
URI
Publication date:
7.10.2024.
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