Abstract
Providing equal access to public service resources is a fundamental goal of democratic societies. Growing research interest in public services (e.g., healthcare, humanitarian relief, elections) has increased the importance of considering objective functions related to equity. This paper studies discrete resource allocation problems where the decision maker is concerned with maintaining equity between some defined subgroups of a customer population, and where non-closed-form functions of equity are allowed. We employ simulation-optimization techniques to develop rigorous algorithms to allocate resources equitably among these subgroups. The presented solutions are associated with probabilistic bounds on solution quality. A full-factorial experimental design demonstrates that our algorithm outperforms competing heuristics and is robust over various inequity metrics. Additionally, we apply our algorithm to a case study of allocating voting machines to election precincts in Franklin County, Ohio.