Optimal Stopping for Dynamic Recruitment Problem with Probabilistic Loss of Candidates

Abstract

A problem that often arises in the recruitment process is that the recruitment firms face possible loss of candidates. The loss of candidates can induce a loss cost to firms which need to restart the recruitment process. In this article, we model the recruitment process as a discrete-time stochastic optimal stopping problem with a finite planning horizon, where candidates may be hired by other firms during the period of waiting for employment with a loss probability. An optimal decision rule is presented to maximize the benefit of the recruitment firm. This decision rule demonstrates that the threshold of direct employment will be reduced as the loss probability (or the loss cost) is increasing. In addition, we find that new applicants are hardly being directly employed when the remaining time to the deadline is very long. Finally, a numerical example is given to illustrate the effectiveness of the proposed decision rule.

Keywords:

• Dynamic programming
• Loss of candidates
• Optimal stopping
• Sequential search
• Stochastic model

Subject Classifications:

• 49L20
• 60G40
• 62L15
• 90C39
• 91B70
• 91D35

ACKNOWLEDGMENTS

The authors thank the editor Nitis Mukhopadhyay and the referees for reviewing the article and their suggestions to improve its layout and formatting.

Notes

This statistical result is publicly available at: http://www.careerbuilder.com/share/ aboutus/pressreleasesdetail.aspx?sd=6/20/2012&id=pr703&ed=12/31/2012.

Recommended by Nitis Mukhopadhyay

Color versions of one or more of the figures in the article can be found online at http://www.tandfonline.com/lsqa.