Managing Learning and Turnover in Employee Staffing

Noah Gans () and Yong-Pin Zhou ()
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Noah Gans: OPIM Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6366
Yong-Pin Zhou: Department of Management Science, School of Business Administration, University of Washington, Seattle, Washington 98195-3200

Operations Research, 2002, vol. 50, issue 6, 991-1006

Abstract: We study the employee staffing problem in a service organization that uses employee service capacity to meet random, nonstationary service requirements. The employees experience learning and turnover on the job, and we develop a Markov Decision Process (MDP) model which explicitly represents the stochastic nature of these effects. Theoretical results show that the optimal hiring policy is of a state-dependent “hire-up-to” type, similar to an inventory “order-up-to” policy. For two important special cases, a myopic policy is optimal. We also test a linear programming (LP) based heuristic, which uses average learning and turnover behavior, in stationary environments. In most cases, the LP-based policy performs quite well, within 1% of optimality. When flexible capacity---in the form of overtime or outsourcing---is expensive or not available, however, explicit modeling of stochastic learning and turnover effects may improve performance significantly.

Keywords: Dynamic programming/optimal control; applications: hierarchical model for manpower planning. Organizational studies; manpower planning: MDP staffing model withlearning and turnover (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (22)

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