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Article: Managing learning and turnover in employee staffing

TitleManaging learning and turnover in employee staffing
Authors
Issue Date2002
PublisherINFORMS. The Journal's web site is located at http://or.pubs.informs.org
Citation
Operations Research, 2002, v. 50 n. 6, p. 991-1006 How to Cite?
AbstractWe 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.
Persistent Identifierhttp://hdl.handle.net/10722/157724
ISSN
2015 Impact Factor: 1.777
2015 SCImago Journal Rankings: 3.528
References

 

DC FieldValueLanguage
dc.contributor.authorGans, Nen_HK
dc.contributor.authorZhou, YPen_HK
dc.date.accessioned2012-08-08T08:55:10Z-
dc.date.available2012-08-08T08:55:10Z-
dc.date.issued2002en_HK
dc.identifier.citationOperations Research, 2002, v. 50 n. 6, p. 991-1006en_HK
dc.identifier.issn0030-364Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/157724-
dc.description.abstractWe 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.en_HK
dc.languageengen_US
dc.publisherINFORMS. The Journal's web site is located at http://or.pubs.informs.orgen_HK
dc.relation.ispartofOperations Researchen_HK
dc.titleManaging learning and turnover in employee staffingen_HK
dc.typeArticleen_HK
dc.identifier.emailZhou, YP: yongpin@hku.hken_HK
dc.identifier.authorityZhou, YP=rp01614en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1287/opre.50.6.991.343-
dc.identifier.scopuseid_2-s2.0-0036878083en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036878083&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume50en_HK
dc.identifier.issue6en_HK
dc.identifier.spage991en_HK
dc.identifier.epage1006en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridGans, N=6602173682en_HK
dc.identifier.scopusauthoridZhou, YP=9037956000en_HK

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