Optimal Hiring and Retention Policies for Heterogeneous Workers Who Learn

Alessandro Arlotto (), Stephen E. Chick () and Noah Gans ()
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Alessandro Arlotto: The Fuqua School of Business, Duke University, Durham, North Carolina 27708
Stephen E. Chick: Technology and Operations Management Area, INSEAD, 77305 Fontainebleau France
Noah Gans: Operations and Information Management Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104

Management Science, 2014, vol. 60, issue 1, 110-129

Abstract: We 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 25(10):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.

Keywords: learning curves; heterogeneous workers; Bayesian learning; call center; hiring and retention; operations management; Gittins index; Bandit problem (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:60:y:2014:i:1:p:110-129

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