Managing learning and turnover in employee staffing
成果类型：
Article
署名作者：
Gans, N; Zhou, YP
署名单位：
University of Pennsylvania; University of Washington; University of Washington Seattle
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.50.6.991.343
发表日期：
2002
页码：
991-1006
关键词：
摘要：
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.