Optimal Hiring and Retention Policies for Heterogeneous Workers Who Learn

• 成果类型: Article
• 署名作者: Arlotto, Alessandro; Chick, Stephen E.; Gans, Noah
• 署名单位: Duke University; INSEAD Business School; University of Pennsylvania
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2013.1754
• 发表日期: 2014
• 页码: 110-129
• 关键词: learning curves; heterogeneous workers; Bayesian learning; call center; hiring and retention; operations management; Gittins index; Bandit problem

摘要

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 Valimaki [Bergemann D, Valimaki 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.