Using client-variance information to improve dynamic appointment scheduling performance

Abstract

Clients of services expect short waiting times and servers desire short periods of non-productive time. One of the areas where this is most important is appointment scheduling systems. Recent research has indicated that using information about clients’ service time variability can simultaneously reduce waiting times and server idle time. In this study, a more realistic, dynamic appointment-scheduling environment is developed and used to analyze several scheduling rules. Additional complexities considered in this study include: continuously distributed service-time variances, special client appointment requests, and appointment-scheduler uncertainty. Results show that rules using client-variance information are still best at minimizing waiting time and idle time with the additional complexities. In fact, these rules perform best when client variance is large. However, on measures related to clients requesting specific appointment slots the results are not as clear cut. A key factor for these measures is the distribution of the desired slots. When the desired slots are near the end of the appointment scheduling period, traditional rules like first-call-first-appointment perform better on client appointment request measures.