论文标题
最佳的多重停止规则用于暖启动顺序选择
Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection
论文作者
论文摘要
在本文中，我们介绍了使用动态编程开发的温暖的动态阈值算法，用于标准在线选择问题的变体。该问题允许在过程开始时自由或已经占用职位。在整个选择过程中，决策者对新候选人进行了一次访问，并揭示了每个候选人的质量分数。基于这些信息，她可以（重新）通过立即做出不可撤销的决定，最多一次分配每项工作。我们放宽了一系列动态编程算法的艰难要求，以完美了解候选人得分的分布，通过为部分和无信息案例展示扩展，在这些情况下，决策者可以在访谈候选人的同时依次学习基本的分数分布。
In this paper we present the Warm-starting Dynamic Thresholding algorithm, developed using dynamic programming, for a variant of the standard online selection problem. The problem allows job positions to be either free or already occupied at the beginning of the process. Throughout the selection process, the decision maker interviews one after the other the new candidates and reveals a quality score for each of them. Based on that information, she can (re)assign each job at most once by taking immediate and irrevocable decisions. We relax the hard requirement of the class of dynamic programming algorithms to perfectly know the distribution from which the scores of candidates are drawn, by presenting extensions for the partial and no-information cases, in which the decision maker can learn the underlying score distribution sequentially while interviewing candidates.
