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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.
Dynamic programming/optimal control, applications: hierarchical model for manpower planning. Organizational studies, manpower planning: MDP staffing model withlearning and turnover
Gans, N., & Zhou, Y. (2002). Managing Learning and Turnover in Employee Staffing. Operations Research, 50 (6), 991-1006. http://dx.doi.org/10.1287/opre.50.6.991.343
Date Posted: 27 November 2017
This document has been peer reviewed.