Analysis of Queue System to Improve the Quality of Service in GraPARI Telkomsel Banda Aceh

Kristoffel C. Pandiangan and Palti Maruli Tua Sitorus
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Kristoffel C. Pandiangan: School of Economic and Business, Telkom University, Indonesia
Palti Maruli Tua Sitorus: School of Economic and Business, Telkom University, Indonesia

International Journal of Business and Economic Affairs (IJBEA), 2017, vol. 2, issue 4, 220-226

Abstract: The problem of queue is how to balance the waiting costs and preventing cost of the queue to gain maximumpro t. Queuing analysis can be done by knowing arrival distribution, service time distribution, queue discipline, andqueuing model. The purpose of the paper is to calculate the optimal number of servers so users do not wait too longin line, know the queue model in accordance with queuing conditions, and improve the eciency and performance ofservers. In processing data, we are using analysis of distribution and M/G/s queuing method. The result of queuingsystem performance based on analysis and calculation shows that the queue model is (M/ G/ 8) :( FIFO/∞/∞). Theaverage time the customer waits in the queue is 17.17 minutes and the average service time per customer is 14.39 minutes.Customer is served for 14.39 minutes but before being served it must wait for 17.17 minutes. This condition indicatesthat waiting time is more than serving time. The system utility level at Gra PARI Banda Aceh is 95.5%, it indicatesthat utility is so high that it should be repaired to optimize the utility. The waiting time in the queue is 17.17 minutes,the waiting time in the system is 31.66 minutes, and the queue length in the system is 17 people. This will make thebottleneck of customers who will queue before being served. Based on the analysis of the new design, it is known thatthe addition of 1 server from 8 to 9 servers is the most optimal solution. The addition of 1 server can reduce the systemutility up to 84.9% and waiting time in the queue is 2.91 minutes, waiting time in the system 17.4 minutes, and the queuelength in the system 9 people. Cost analysis also shows the solution can reduce the cost of queue, so the optimal modelhas been found in this study. Therefore, optimal queuing conditions can help companies to improve service quality.

Keywords: Queuing system; Queuing model; M/G/8; FIFO; Service quality (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:aya:ijbeaa:2017:p:220-226

DOI: 10.24088/IJBEA-2017-24001

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