Abstract—Queue management is a crucial part of service industry. Business has to deal with the uncertainty of arrival customer and the trade-off between the costs of providing capacity and customer satisfaction. Matching service capacity with the arrival customer is difficult so the system utilization is low in the low periods and customers have to wait for a long time in the peak period. Due to long waiting, some customer abandons the queue before receiving service. Customer abandonment affects business revenue and the system utilization. To relieve the effect of the customer abandonment, this paper aims to propose Artificial Neural Network based waiting time predictor with queue reservation system. The proposed system increases the system utilization and increase customer satisfaction at the same time. Instead of abandonment, customers can reserve their place in queue if the waiting time is too long and they can do other activities while waiting. Moreover, the proposed system provides the accurate estimated waiting time to each customer instead of queue-length. The accurate waiting time enables arrival customers to better decide on the reservation options. 95% of our predicted waiting time is accurate within 5 minutes. Comparing to the system without the queue reservation system, the system utilization and the number of the arrival customer completed service improve 13% and 54% respectively.
Index Terms——Queue management system, waiting time prediction, queue reservation system, artificial neural network.
P. Anussornnitisarn is with the Department of Industrial Engineering, Faculty of Engineering, Kasetsart Univeristy, Thailand (e-mail: firstname.lastname@example.org).
V. Limlawan is with International Graduated Program in Industrial Engineering, Faculty of Engineering, Kasetsart Univeristy, Thailand (e-mail: email@example.com).
Cite: V. Limlawan and P. Anussornnitisarn, "Enhance System Utilization and Business Revenue with AI-based Queue Reservation System," International Journal of Machine Learning and Computing vol. 11, no. 3, pp. 236-241, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).