A Sequential Model for High-Volume Recruitment Under Random Yields

Abstract

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.