Workforce grouping and assignment with learning-by-doing and knowledge transfer |
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Authors: | Huan Jin Barrett W. Thomas |
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Affiliation: | 1. Ningbo Supply Chain Innovation Institute China, MIT Global SCALE Network , .;2. Department of Management Sciences, Tippie College of Business, University of Iowa , Iowa City, IA, USA. |
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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. |
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Keywords: | knowledge transfer learning curves integer programming worker assignment productivity management |
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