Association for Public Policy Analysis & Management

The ability to make data-driven decisions about scheduling clients, volunteers, and staff is an important step to help nonprofit or government funded programs run more efficiently.  Using the operations research method of queuing theory is a fairly simple analytical approach that policy and program analysts can easily apply.  Additionally, the data required for applying this type of model is often already collected at nonprofit and government programs.

This paper is a case study of how to apply a memoryless queueing model to optimize the scheduling of staff working hours, the scheduling of volunteer time slots, and opening hours for a Volunteer Income Tax Assistance (VITA) site.  Staff at the VITA site collected data on client sign-in times, waiting times, service delivery duration, and departure times over four weeks of the 2015 tax season.  The results of the queueing model showed that the agency would benefit from altering their opening time, scheduling more volunteers and staff in the beginning of the day, and reducing the volunteers and staff scheduled for the afternoon. Optimizing the schedule at VITA sites may lead to an increase in the number of tax returns prepared, an increase in client satisfaction due to reduced waiting times, and an increase in volunteer retention and satisfaction when their time is under- or over-utilized at the site.