Managing Learning and Turnover in Employee Staffing
Operations Research, Vol. 50, No. 6, December 2002
Posted: 19 Nov 2006
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 capacityin the form of overtime or outsourcingis expensive or not available, however, explicit modeling of stochastic learning and turnover effects may improve performance significantly.
Keywords: Dynamic programming, optimal control, hierarchical model for manpower planning, organizational studies, manpower planning
JEL Classification: D24, M12
Suggested Citation: Suggested Citation