Track: Optimization

Abstract

Queueing is a common problem for busy banks, causing long waiting times for customers. This study focuses on a leading bank in Bangladesh that sees high volumes of customers daily. The goal is to simulate the bank's service system to determine if an additional server is needed during peak hours. Additionally, removing the extra servers to get the optimum service level as the servers remain idle when there are not many customers in a bank is mainly for the cost minimization of the banking operations by maintaining its optimum utilization. Here, a discrete event simulation (DES) model is applied to analyze the queueing performance over time. Key aspects like arrival rates, service times, queue capacity, and customer tolerance are represented. The simulation runs for an extended period to mimic real customer traffic. Performance metrics including average waiting time, queue length, resource utilization, number of busy servers, idle servers, and customer complaints are then quantified. The validated simulation accurately portrays the existing operation and allows testing changes by adding or removing the servers resulting in a reduction of 31.8% in queue length and 33.76% in service time, alongside a notable 20.48% improvement in service utilization. Findings suggest this simulation yields better resource management by speeding up service that proves effective for evaluating complex queueing systems and informing strategic decisions through quantitative analysis of alternatives. Overall, the analysis aimed to enhance seamless bank operations, customer satisfaction, and experience at the busy bank location through data-driven recommendations on queue management.