Optimal appointment reminder sending strategy for a single service scenario with customer no-show behaviour

Abstract

Making an appointment is an effective way to balance the supply and demand in service industries. However, people may not show up for their appointments at the scheduled time. Undoubtedly, sending a reminder to ask for a clear response for each appointment can lower the no-show rate and provide more time for service providers to perform other activities. Therefore, the most important variable is to determine when the reminders should to be sent. In this paper, we study the optimal appointment reminder sending strategy for a single service scenario with customer no-show behaviour. Through discretising the decision process, a dynamic programming model is formulated. Then the optimal time to send a reminder for each appointment is calculated. We prove that there exists an optimal time to send a reminder for each appointment and that the earlier an appointment is made, the earlier a reminder should be sent. Furthermore, our numerical studies show that there exists an optimal appointment time window for a service with a given arrival rate and no-show rate. In addition, the higher the no-show rate of a customer is, the later a reminder should be sent. Based on the optimal reminder sending strategy, the expected service utilisation can be improved compared to no reminders or sending reminders 24 h before the scheduled time. Especially, the increase in the expected service utilisation rate becomes more significant when the arrival rate decreases and the no-show rate increases.

Keywords:

• OR in service industries
• dynamic programming
• optimal reminder sending strategy
• no-show behaviour
• appointment

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported partially by the Natural Science Foundation of Zhejiang Province [grant number LY18G010019]; National Natural Science Foundation of China [grant number 71301148], [grant number 71401158], [grant number 71301147], [grant number 71371170].