A multi-channel queue model to optimize service level and staff availability in bank industry

Staff scheduling in service organizations like banks, stores, call centers, and emergency centers is critical due to direct customer interaction and uncertain service demand. This research presents a multi-objective model for scheduling bank staff, focusing on uncertain customer arrival and service rates. The model aims to optimize customer service efficiency and maximize staff satisfaction through three objective functions: minimizing the customer waiting queue length (using an M/M/C system), minimizing the number of assigned employees, and maximizing employee satisfaction by considering preferred working times. By simulating Poisson distribution for client arrival and service times, we predicted the bank’s queue system performance and optimized staffing levels using the proposed model. Tested with real data from Agribank in Iran, the results showed an 8% reduction in customer waiting times and a 53% increase in employee satisfaction, demonstrating significant improvements in service efficiency and workplace morale. These percentages highlight the model’s ability to effectively balance operational efficiency and employee well-being, facilitated by its transparent work pattern structure. Given the NP-hard nature of the model, we employed a meta-heuristic approach (NSGA-II) and GAMS with the ɛ -constraint method to solve it. Comparative results indicated that NSGA-II outperformed GAMS in both solution quality and computational time.