A study on pre-charging strategy for appointment scheduling problem with no-shows

Abstract

No-shows of customers are common in many appointment-based service systems that often cause severe idling and underutilization of capacity. This paper studies a new strategy that pre-charges customers for a deposit to lock up appointments, which differs from previous studies on overbooking strategies that reserve more appointments than its capacity to hedge against the loss of revenue due to customer no-shows. Moreover, the new strategy is more favorable in practice because it improves the satisfaction and loyalty of customers in the long-term running. We analyze two versions of the pre-charge strategy including (i) offering a partial or full refund for cancellation (to-refund policy), and (ii) offering no refund for cancellation (no-refund policy). We consider two settings of the appointment scheduling problem including a static setting in which appointment decisions are made at the beginning of the day, and a dynamic setting in which appointment decisions are made sequentially within the day. The two settings are formulated by an analytic model and a Markov decision process model, respectively. The analytical results reveal that the pre-charging strategy is consistently more profitable than the overbooking strategy even for a fully refundable deposit case, and the to-refund policy can be more profitable than the no-refund policy. Moreover, we show that the profitability of the to-refund policy continues to hold in a dynamic setting in which the service capacity is limited, the arrivals are dynamic, and the report of cancellation is uncertain. Finally, we implement a series of numerical experiments based on real data in the context of managing patient appointments for a large clinic and demonstrate the managerial implications.

Keywords:

• Healthcare operations
• appointment scheduling
• no-shows
• pre-charging strategy
• cancellation behavior

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 This normal distribution approximation is appropriate because each department has capacity that is more than 100 outpatient visits in the hosptial we studied.

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (No. 72001187, 71821002, 71801058) and the Fundamental Research Funds for the Central Universities.