A Sequential Model for High-Volume Recruitment Under Random Yields
研究了大规模招聘中多阶段、随机产出和预设目标的问题，提出近似最优的决策工具，帮助确定各阶段发offer数量和招聘阶段数，案例表明能改善招聘结果。
A Sequential Model for High-Volume Recruitment Under Random Yields High-volume recruiting is challenging, as it involves hiring a larger number of people in a short amount of time. In “A Sequential Model for High-Volume Recruitment Under Random Yields,” Du, Li, and Yu model a high-volume recruiting process as a large-scale dynamic program. Their model captures three important features of high-volume recruiting: multiple phases, random yields, and a preset hiring target. They provide a decision tool to answer practical questions about the number of offers to be made in each phase and the number of phases in a recruitment season. To solve the dynamic program, they rely on approximations, and the approximations are asymptotically optimal when the volume is large. Their simulation studies confirm the convergence results. They illustrate how their modeling framework can be put into practice in a case study, which shows that their decision tool can improve the recruiting outcome.