Managing learning and turnover in employee staffing

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 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 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.