Managing Learning and Turnover in Employee Staffing

Operations Research, Vol. 50, No. 6, December 2002

Posted: 19 Nov 2006

See all articles by Noah Gans

Noah Gans

University of Pennsylvania - Operations & Information Management Department

Yong-Pin Zhou

University of Washington Business School

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, hierarchical model for manpower planning, organizational studies, manpower planning

JEL Classification: D24, M12

Suggested Citation

Gans, Noah and Zhou, Yong-Pin, Managing Learning and Turnover in Employee Staffing. Operations Research, Vol. 50, No. 6, December 2002, Available at SSRN: https://ssrn.com/abstract=945282

Noah Gans

University of Pennsylvania - Operations & Information Management Department ( email )

Philadelphia, PA 19104
United States

Yong-Pin Zhou (Contact Author)

University of Washington Business School ( email )

Box 353200
University of Washington Business School
Seattle, WA 98195-3200
United States
(206) 221-5324 (Phone)
(206) 543-3968 (Fax)

HOME PAGE: http://faculty.washington.edu/yongpin

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