A Machine Learning Approach to Waiting Time Prediction in Queueing Scenarios

Physically queueing is a reality on many industries that provide services or sell goods. Waiting in a queue can be stressful and exhausting for the clients because of the enforced idle time, and may lead to decreased customer satisfaction. Queueing theory has been widely used to assess client waiting times, to optimize staff schedules, and to increase the robustness of a queueing system against a variable demand for service. In this paper, we are exploring how multiple industries that require queues can benefit from machine learning to predict the clients' waiting times. We begin by predicting waiting times on bank queues, and then we propose how the procedure can be generalized to more industries and automatized. A publicly available dataset containing entries of people queueing in banks is initially utilized, and after training a fully connected neural network, a mean absolute error of 3.35 minutes in predicting client waiting times was achieved. We are then presenting a web application that is managing queues of different scenarios and industries. The queues may have unique parameters, and the system can adapt to each queue as it creates a per queue optimally trained neural network for waiting time prediction. The use and the capabilities of the system are validated with the use of a simulator. Machine learning, therefore, proves to be a viable alternative to queueing theory for predicting waiting time.