论文标题
部分可观测时空混沌系统的无模型预测
Selection and Ordering Policies for Hiring Pipelines via Linear Programming
论文作者
论文摘要
通过雇用管道的启发，我们研究了三个选择和订购问题，其中必须采访或发送有限职位的申请人。面试/发送优惠的时间预算有限，每个面试/优惠都会有随机意识到发现申请人的质量或接受决定，从而导致计算上具有挑战性的问题。在第一个问题中，我们研究顺序访谈并表明，在面试后必须立即提出的计算障碍，非适应性政策几乎是最佳的，假设要约始终被接受。我们进一步展示了如何将此策略用作获得PTA的子例程。在第二个问题中，我们假设申请人已经接受了采访，但只接受了一些概率；我们制定了一项计算处理策略，该策略可以并行为不同的位置提供报价，即使位置是异质的，也可以使用，并且相对于可以一一将优点总数提供相同总数的策略而言几乎是最理想的。在第三个问题中，我们引入了一个简约的超预订模型，其中所有要约都必须同时发送，并且在职位数量之外的每个接受情况都会产生线性惩罚；我们几乎就实际动机的价值策略的表现提供了几乎紧密的界限。 总而言之，基于线性编程，我们的论文以三种不同的招聘问题采用统一的方法。由于Purohit等人，我们在前两个问题中的结果概括并改善了现有的保证。 （2019年）在1/8至1/2之间至少为1-1/e的新保证。我们还可以从数字上比较候选人的三种不同设置（同时，并行或同时），从而提供了有关公司何时应该偏爱每个人的洞察力。
Motivated by hiring pipelines, we study three selection and ordering problems in which applicants for a finite set of positions must be interviewed or sent offers. There is a finite time budget for interviewing/sending offers, and every interview/offer is followed by a stochastic realization of discovering the applicant's quality or acceptance decision, leading to computationally challenging problems. In the first problem, we study sequential interviewing and show that a computationally tractable, non-adaptive policy that must make offers immediately after interviewing is near-optimal, assuming offers are always accepted. We further show how to use this policy as a subroutine for obtaining a PTAS. In the second problem, we assume that applicants have already been interviewed but only accept offers with some probability; we develop a computationally tractable policy that makes offers for the different positions in parallel, which can be used even if positions are heterogeneous, and is near-optimal relative to a policy that can make the same total number of offers one by one. In the third problem, we introduce a parsimonious model of overbooking where all offers must be sent simultaneously and a linear penalty is incurred for each acceptance beyond the number of positions; we provide nearly tight bounds on the performance of practically motivated value-ordered policies. All in all, our paper takes a unified approach to three different hiring problems, based on linear programming. Our results in the first two problems generalize and improve the existing guarantees due to Purohit et al. (2019) that were between 1/8 and 1/2 to new guarantees that are at least 1-1/e. We also numerically compare three different settings of making offers to candidates (sequentially, in parallel, or simultaneously), providing insight into when a firm should favor each one.
