Name
Abstract | ||
|---|---|---|
AbstractWe study the hiring and retention of heterogeneous workers who learn over time. We show that the problem can be analyzed as an infinite-armed bandit with switching costs, and we apply results from Bergemann and Välimäki [Bergemann D, Välimäki J 2001 Stationary multi-choice bandit problems. J. Econom. Dynam. Control 2510:1585--1594] to characterize the optimal hiring and retention policy. For problems with Gaussian data, we develop approximations that allow the efficient implementation of the optimal policy and the evaluation of its performance. Our numerical examples demonstrate that the value of active monitoring and screening of employees can be substantial. This paper was accepted by Yossi Aviv, operations management. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1287/mnsc.2013.1754 | Periodicals |
Keywords | Field | DocType |
learning curves,heterogeneous workers,Bayesian learning,call center,hiring and retention,operations management,Gittins index,Bandit problem | Mathematical optimization,Economics,Bayesian inference,Gittins index,Gaussian,Learning curve,Active monitoring | Journal |
Volume | Issue | ISSN |
60 | 1 | 0025-1909 |
Citations | PageRank | References |
4 | 0.42 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro Arlotto | 1 | 19 | 5.19 |
| Stephen E. Chick | 2 | 1127 | 152.40 |
| Noah Gans | 3 | 613 | 66.60 |