"Analytically, our methods are more robust and the perspective gained by using simulation has been well-received by the agency."
Carrie Thompson, Senior Policy Analyst, Virginia Department Of Motor Vehicles
Once normalized, the wait time benchmark was set and the full model was run, using an algorithm designed to identify the lowest number of staff required to accomplish an average wait time at each hour interval.
For each trial, 100 runs were calculated to produce hourly average wait times. The scenario was evaluated chronologically, and at the earliest instance where the average wait time exceeded the benchmark, the staff resource was increased by one.
Further trials were automatically conducted until staffing levels accomplished the wait time benchmark for a full day of operation. Upon completion, recommendations were evaluated against existing limitations such as maximum staff available and service window availability.
For example, if the model returned a staffing recommendation of 25 in the third hour, but only 12 service windows are available at the site, the staffing numbers were manually reduced to reflect that limitation.
Another trial was completed after manual adjustments were made. If the average wait time met the 20 minute goal, recommended staffing levels were compared to the actual staffing levels, and the variance was reported as the staffing adjustment necessary for each hourly interval to achieve the benchmark.