| 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 research studies discrete-resource allocation problems where the decision maker is concerned with maintaining equity between some defined subgroups of a customer population. In particular, we apply resource-allocation models to improve equity in two specific applications: voting systems and health care. We employ simulation-optimization techniques and Markov-decision-process models to develop rigorous algorithms to allocate resources equitably among these subgroups. The presented solutions are associated with bounds on solution quality, some of which are guaranteed to be optimal. Various numerical tests demonstrate that our algorithms outperform competing heuristics and are robust over various inequity metrics. We apply our algorithms to real case studies using empirical data for a voting-machine-allocation problem in Franklin County, Ohio, and to develop an admission-control policy for a cardiac intensive care unit at Mount Sinai Medical Center in New York. |
