Allocation of scarce healthcare resources under limited logistic and infrastructural facilities is a major issue in the modern society. We consider the problem of allocation of healthcare resources like vaccines to people or hospital beds to patients in an online manner. Our model takes into account the arrival of resources on a day-to-day basis, different categories of agents, the possible unavailability of agents on certain days, and the utility associated with each allotment as well as its variation over time. We propose a model where priorities for various categories are modelled in terms of utilities of agents. We give online and offline algorithms to compute an allocation that respects eligibility of agents into different categories, and incentivizes agents not to hide their eligibility for some category. The offline algorithm gives an optimal allocation while the on-line algorithm gives an approximation to the optimal allocation in terms of total utility. Our algorithms are efficient, and maintain fairness among different categories of agents. Our models have applications in other areas like refugee settlement and visa allocation. We evaluate the performance of our algorithms on real-life and synthetic datasets. The experimental results show that the online algorithm is fast and performs better than the given theoretical bound in terms of total utility. Moreover, the experimental results confirm that our utility-based model correctly captures the priorities of categories

翻译：在有限的后勤与基础设施条件下，如何分配稀缺的医疗资源是现代社会的重大课题。本文研究在线方式下向人群分配疫苗或向患者分配病床等医疗资源的问题。我们的模型考虑了资源每日到达、不同类别的主体、主体在特定日期可能不可用、每次分配的效用及其随时间变化等因素。我们提出一种模型，其中各类别的优先级通过主体效用进行建模。我们给出在线与离线算法，以计算满足主体在不同类别中的资格要求、并激励主体不隐瞒其特定类别资格的分配方案。离线算法给出最优分配，而在线算法在总效用上给出最优分配的近似解。我们的算法高效且能维持不同类别主体间的公平性。该模型还可应用于难民安置、签证分配等其他领域。我们在真实与合成数据集上评估算法性能，实验结果表明在线算法速度快，且在总效用上优于给定理论界。此外，实验结果证实了我们基于效用的模型能准确反映各类别的优先级。