Workforce grouping and assignment with learning-by-doing and knowledge transfer

Huan Jin, Mike Hewitt and Barrett W. Thomas

International Journal of Production Research, 2018, vol. 56, issue 14, 4968-4982

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.

Date: 2018
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DOI: 10.1080/00207543.2018.1424366

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