热启动序贯选择问题及其多轮扩展
The Warm-starting Sequential Selection Problem and its Multi-round Extension
摘要：
在顺序选择问题（SSP）中，当候选人随机到达面试地点时，需要立即做出不可撤销的决定。标准的SSP变体，如众所周知的秘书问题，从一个空的选择集（冷启动）开始，并在单个候选集（单轮）上执行一次选择过程。在本文中，我们解决这两个限制。首先，我们介绍了新的热启动SSP（WSSP）的设置，它认为手头的参考集，一组先前选定的项目的给定质量，并试图更新最佳设置（重新）分配每个工作最多一次。我们采用了基于截止值的方法来优化基于排名的目标函数的最终分配的工作。在我们的技术贡献中，我们提供了关于所提出的WSSP设置的分析结果，我们引入了基于截止值的成本最小化（CCM）算法（以及低故障CCM，其对高辞职率更鲁棒），该算法由于我们提出的翻译方法而适应参考集质量的变化。最后，我们在多轮设置中实现和测试CCM，这对于现实世界的应用场景特别有趣。
英文摘要：
In the Sequential Selection Problem (SSP), immediate and irrevocable decisions need to be made as candidates randomly arrive for a job interview. Standard SSP variants, such as the well-known secretary problem, begin with an empty selection set (cold-start) and perform the selection process once over a single candidate set (single-round). In this paper we address these two limitations. First, we introduce the novel Warm-starting SSP (WSSP) setting which considers at hand a reference set, a set of previously selected items of a given quality, and tries to update optimally that set by (re-)assigning each job at most once. We adopt a cutoff-based approach to optimize a rank-based objective function over the final assignment of the jobs. In our technical contribution, we provide analytical results regarding the proposed WSSP setting, we introduce the algorithm Cutoff-based Cost Minimization (CCM) (and the low failures-CCM, which is more robust to high rate of resignations) that adapts to changes in the quality of the reference set thanks to the translation method we propose. Finally, we implement and test CCM in a multi-round setting that is particularly interesting for real-world application scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
