Decision making in the public sector centers on delivering resources and
services for the common good, emphasizing an expansive set of objectives such
as equity and efficiency, beyond immediate short term returns to reflect the
broader cares of society and public beneficiaries. Cost-benefit analysis is a
prevailing decision-making framework in the public sector that often uses the
benefit to cost ratio (BCR) to compare viable alternatives, yet no systematic
framework exists for evaluating many alternatives beyond the status quo of
doing nothing. We propose a new framework to maximize the BCR for public sector
decisions, seeking the largest improvement per marginal deployment of capacity.
Requiring a status quo representable through (constrained) decision variables,
the framework is generally applicable and useful to a broad set of decision
contexts that involve maximizing the BCR for marginal deployments of resources.
We demonstrate the applicability of our framework on a compelling case study
for the New York City runaway and homeless youth shelter system, an area of
high societal need. We represent this problem as a mixed integer linear
fractional program (MILFP) and employ Dinkelbach's algorithm that converts the
MILFP to a series of linearized mixed-integer optimization problems, making our
approach tractable for fairly large problem instances. Our optimization-based
algorithmic framework yields data-informed recommendations for making New York
City shelter expansion decisions to better serve runaway and homeless youth,
and generalizes to reveal managerial insights for optimizing the BCR. More
broadly, our algorithmic decision making framework allows for iteration and
comparison across multiple potential constraints ensuring action away from the
status quo, thereby empowering effective assessment of marginal deployment of
additional resources