Client variance and scheduler uncertainty in dynamic appointment scheduling

This study examines the effects of using continuous distribution for client service time variances in dynamic appointment scheduling. Previous research considered only a discrete variance distribution. The primary performance objective is to minimize the combined cost of client waiting and server idle time. The relaxed variance distribution assumption is particularly pertinent to the class of scheduling rules recently developed in Klassen and Rohleder, which use client service time variances. From [6], the low variance beginning (LVBEG) rule was selected as representative for this study. Additionally, scheduler uncertainty when classifying clients is added as an experimental factor. The results show LVBEG still performs well with the more realistic assumptions considered in this study. It is statistically the best rule for all levels of service time variance dispersion. Also, while greater scheduler uncertainty lessens LVBEG's effectiveness, even with the highest level of uncertainty, LVBEG was still better than any other scheduling rule tested in this study.