Optimum Shift Design
With the Disabled Adult Transit Service's (DATS) collective agreement expiring in December 2009, it is advantageous for DATS to perform an analysis of their current workforce performance. As a result, our client asked us to analyze their current shift scheduling system in order to build for them a shift scheduling model that is able to quantify the benefits from varying combinations of full-time and part-time operators (with an overtime reduction perspective in mind as well).
We built such a model, and then analyzed DATS' current shift schedule against other possible schedules that met a series of supply constraints. The model was optimized to meet demand while also minimizing daily operator costs. The utilization rate of these operators was another key performance indicator in deciding on a recommended shift schedule for our client. Our analysis revealed that a greater number of part-time shifts implemented into DATS' schedule reduced their daily costs. As well, more part-time operators reduced overtime costs, and increased productivity.
DATS? demand has experienced a quick and significant increase. As a result, they need a means to accurately and reliably forecast this demand in order to effectively manage it. The city of Edmonton's official population forecast to 2030 predicts an aggressive aging of its population under two scenarios (Base Case and High Scenario). Since age, along with gender, are leading indicators for, as well as key drivers of, the disabled population, Edmonton's aggressive aging will also result in an increase in its disabled population. Assuming that this relationship is linear, a simple linear forecast revealed that an almost doubling of Edmonton's 65 and over age group resulted in a 62% and 72% increase in its disabled population under the Base Case and High Scenario, respectively. Since Edmonton's disabled population is a key driver of the demand for DATS, such an increase will also drive an aggressive increase in this demand. Again, a simple linear forecast revealed that the demand for DATS increased by 234% and 256%.
If DATS accepts all of this forecasted demand as clients, and if its total number of clients is the exclusive driver of its total number of employees, vehicles, and operating expenses, then the impacts of this forecasted demand on DATS' ongoing operations are significant. Under the Base Case, DATS' number of employees will increase by 268%, vehicles by 215%, and its operating expenses by 197%; while under the High Scenario, employees by 293%, vehicles by 235%, and its operating expenses by 215%. As a result, it is essential that DATS effectively manages these potential impacts with three cost containment strategies: redirecting this forecasted demand to other non-city organizations that also provide assisted transportation services; employing a more optimal mix of both full-time and part-time operators in conjunction with a more optimal shift schedule; and modifying DATS' current approach of providing door to door service into a more centralized pickup and drop off service.