We consider online resource allocation under a typical non-profit setting,
where limited or even scarce resources are administered by a not-for-profit
organization like a government. We focus on the internal-equity by assuming
that arriving requesters are homogeneous in terms of their external factors
like demands but heterogeneous for their internal attributes like demographics.
Specifically, we associate each arriving requester with one or several groups
based on their demographics (i.e., race, gender, and age), and we aim to design
an equitable distributing strategy such that every group of requesters can
receive a fair share of resources proportional to a preset target ratio. We
present two LP-based sampling algorithms and investigate them both
theoretically (in terms of competitive-ratio analysis) and experimentally based
on real COVID-19 vaccination data maintained by the Minnesota Department of
Health. Both theoretical and numerical results show that our LP-based sampling
strategies can effectively promote equity, especially when the arrival
population is disproportionately represented, as observed in the early stage of
the COVID-19 vaccine rollout.

本研究考虑在非营利性组织的情境下进行在线资源分配，通过使用基于样本的线性规划算法实现了公平分配策略，并将其应用于实际的 COVID-19 疫苗接种数据集，结果表明该算法可以有效提高公平性。