Queue System Analysis in a Selected Branch of Mellat Bank

Document Type : Original Article

Authors

1 Corresponding Author. Assistant Professor of Operation and Information Technology Management, Kharazmi University, Tehran, Iran. farzad.haghighi@gmail.com

2 Master of Industrial Management, Department of Information Technology Management, Kharazmi University, Tehran, Iran. farzan_eb@yahoo.com

10.22091/jemsc.2026.15054.1337

Abstract

In today’s highly competitive business environment, service organizations are increasingly driven by the dual objective of delivering superior customer service while simultaneously minimizing operational expenditures. For banks, which face intense competition from both traditional financial institutions and emerging fintech companies, achieving this balance is essential for long-term sustainability and profitability. Optimizing service delivery without compromising quality requires a deep understanding of customer flow dynamics and the cost structure underlying service operations. This study addresses this challenge by focusing on a bank branch setting, where queuing theory serves as a powerful analytical tool. The objective is to propose and evaluate a set of approaches for determining the cost parameters associated with the bank’s service system. These parameters are critical inputs for optimizing the trade-off between customer waiting time and idle server costs, a core concern in operations management. To this end, a comprehensive data collection process is conducted using the bank’s existing queue ticket numbering system, which records detailed logs of customer arrivals and service completions. These data are instrumental in fitting appropriate statistical distributions for both inter-arrival and service times. The extracted arrival and service rates enable a precise characterization of the system’s stochastic behavior, laying a robust foundation for further quantitative analysis. Subsequently, the total queuing cost is formulated by integrating both customer waiting costs and server operating costs. A sensitivity analysis is then performed to assess how variations in the number of servers and service rates affect key performance indicators, such as average waiting time, queue length, and system utilization. The analysis reveals that while increasing the number of servers up to two provides marginal improvements, the most substantial efficiency gains are achieved when an increase in service rate is coupled with the addition of service counters.

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References

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