http://www.iaeme.com/IJARET/index.asp 218 editor@iaeme.com International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 10, October 2020, pp. 218-228, Article ID: IJARET_11_10_022 Available online at http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=10 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: 10.34218/IJARET.11.10.2020.022 © IAEME Publication Scopus Indexed OPTIMIZATION APPROACH FOR EXAMINING THE M/M/C QUEUING MODEL FOR BALKING AND RENEGED CUSTOMERS BY HYBRID APPROACH D. Anandakumar Assistant Professor, Department of Mathematics, Sri Krishna College of Engineering & Technology, Coimbatore, TamilNadu, India. Dr. S. Palaniammal Professor & Principal (Supervisor), Department of Mathematics, Sri Krishna Adithya College of Arts & Science, Coimbatore, TamilNadu, India. Dr. N. Nagaveni Professor & Head (Joint Supervisor), Department of Mathematics, Coimbatore Institute of Technology, Coimbatore, TamilNadu, India. ABSTRACT The queuing model of M/M/c is offered with c number of non-reliable servers where the customers may decide to balk or renege. The projected method examines the multi-working server vacation models with parameters like reneging, balking and failure by Smoothed Function (SF) and Quasi-Newton algorithm (QN). The metrics of the system are clearly illustrated in the form of computation. The optimal values are determined by the cost method for the total service, rate of service rate and the failure and repair at the reduced cost. The difficulty arises when there are multiple networks with queue and high queuing length. Three types of mechanisms are developed for this type of problem for minimizing the delays. The routing problems can be resolved by Smoothed function. The constraints are the length of the queue. The Quasi-Newton algorithm is designed in a way that delays are reduced. The results are tested by considering the performance of both the algorithms. In the vacation model, the cost of serving the customer should be low while completing the service. If there are a greater number of customers in the system and there may be an interruption in vacation because of the busy state. With this model, another work is projected for cost analysis. The task of cost analysis is executed by the Particle Swarm Optimization (PSO) method.