An overlapping appointment scheduling model with stochastic service time

This research addresses an overlapping appointment scheduling (OLAS) model to minimize patient wait time and doctor idle time in an outpatient healthcare clinic when a stochastic service time is applied. Efficient appointment scheduling is based on achieving an optimal balance between doctor utilization and patient satisfaction. As a result, the OLAS model has been proposed to find the optimal overlap period between patient appointment and allocated service times. An mathematical model is developed to minimize the total cost of patient waiting and doctor idle time, which has been analyzed with an assumption that the service time is followed by a uniform distribution. In addition, a discrete event simulation model is developed to verify the optimal overlap period driven from the proposed OLAS model and evaluate the effect of cost reduction for varying overlap periods. The experimental results indicate that the optimal environment for applying an OLAS model is an outpatient clinic with a high cost ratio, long appointment lengths, and a high coefficient of variation. Also, the results indicate that the utilization of overlapping scheduling can lead to a 40%-70% reduction in costs when based on patient waiting and doctor idle time.