论文标题
蒙特卡洛树搜索的任何时间扩大医疗居住匹配项
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search
论文作者
论文摘要
本文考虑了在双面比赛中的容量扩展问题，允许决策者分配一些额外的座位以及标准座位。在医疗居住比赛中，每家医院接受有限的医生。这种容量限制通常是事先给出的。但是，这种外源性约束会损害医生的福利。一些受欢迎的医院不可避免地会驳斥他们最喜欢的医生。同时，通常情况下，医院也受益于接受一些额外的医生。为了解决该问题，我们提出了一种任何时间置信树搜索容量扩展空间的任何时间方法，每个置信度扩展的空间都有延期接受方法可以找到的居民最佳稳定分配。构建良好的搜索树表示可以显着提高所提出方法的性能。我们的仿真表明，所提出的方法比基于混合企业编程的精确方法确定了几乎最佳的能力扩展，其计算预算明显较小。
This paper considers the capacity expansion problem in two-sided matchings, where the policymaker is allowed to allocate some extra seats as well as the standard seats. In medical residency match, each hospital accepts a limited number of doctors. Such capacity constraints are typically given in advance. However, such exogenous constraints can compromise the welfare of the doctors; some popular hospitals inevitably dismiss some of their favorite doctors. Meanwhile, it is often the case that the hospitals are also benefited to accept a few extra doctors. To tackle the problem, we propose an anytime method that the upper confidence tree searches the space of capacity expansions, each of which has a resident-optimal stable assignment that the deferred acceptance method finds. Constructing a good search tree representation significantly boosts the performance of the proposed method. Our simulation shows that the proposed method identifies an almost optimal capacity expansion with a significantly smaller computational budget than exact methods based on mixed-integer programming.
