Journal article
Workforce grouping and assignment with learning-by-doing and knowledge transfer
International journal of production research, Vol.56(14), pp.4968-4982
07/18/2018
DOI: 10.1080/00207543.2018.1424366
Abstract
We consider a workforce allocation problem in which workers learn both by performing a job and by observing the performance of and interacting with co-located colleagues. As a result, an organisation can benefit from both effectively assigning individuals to jobs and grouping workers into teams. A challenge often faced when solving workforce allocation models that recognise learning is that learning curves are non-linear. To overcome this challenge, we identify properties of an optimal solution to a non-linear programme for grouping workers into teams and assigning the resulting teams to sets of jobs. With these properties identified, we reformulate the non-linear programme to a mixed integer programme that can be solved in much less time. We analyse (near-)optimal solutions to this model to derive managerial insights.
Details
- Title: Subtitle
- Workforce grouping and assignment with learning-by-doing and knowledge transfer
- Creators
- Huan Jin - Ningbo Supply Chain Innovation Institute China, MIT Global SCALE Network , .Mike Hewitt - Loyola University ChicagoBarrett W. Thomas - University of Iowa
- Resource Type
- Journal article
- Publication Details
- International journal of production research, Vol.56(14), pp.4968-4982
- Publisher
- Taylor & Francis
- DOI
- 10.1080/00207543.2018.1424366
- ISSN
- 0020-7543
- eISSN
- 1366-588X
- Grant note
- CMMI-1266010 / Division of Civil, Mechanical and Manufacturing Innovation (10.13039/100000147) National Science Foundation (10.13039/100000001)
- Language
- English
- Date published
- 07/18/2018
- Academic Unit
- Bus Admin College; Business Analytics
- Record Identifier
- 9984380555602771
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