Dynamic Staffing of Volunteer Gleaning Operations

Abstract

Gleaning programs organize volunteer gleaners to harvest leftover crops that are donated by farmers for the purpose of feeding food-insecure individuals. Thus, the gleaning process simultaneously reduces food waste and food insecurity. However, the operationalization of this process is challenging because gleaning relies on two uncertain sources of input: the food and labor supplies. The purpose of this paper is to increase the volume of fresh food gleaned by better managing the uncertainties in the gleaning operation. We develop a model to capture the uncertainties in food and labor supplies and seek a dynamic volunteer staffing policy that maximizes the long run average volume of food gleaned. The exact analysis of the staffing problem seems intractable. Therefore, we resort to an approximation in the heavy traffic regime. In that regime, we characterize the system dynamics of the gleaning operation and derive the optimal staffing policy in closed form. The optimal policy is a nested threshold policy that depends on the number of available gleaners and the backlog of gleaning donations. A numerical study using data calibrated from a gleaning organization in the Boston area shows that the dynamic staffing policy we propose can make meaningful improvements to gleaning output over a static policy. To achieve these improvements, no capital or major process changes would be required - only some small changes to the staffing level requests.