Optimal Stopping for Dynamic Recruitment Problem with Probabilistic Loss of Candidates

Chi ZHOU, Wansheng TANG*, Ruiqing ZHAO

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)187-210
Number of pages24
JournalSequential Analysis
Issue number2
Early online date18 May 2015
Publication statusPublished - 2015
Externally publishedYes


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


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