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

Huan Jin, Mike Hewitt, Barrett W. Thomas

Research output: Journal PublicationArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)4968-4982
Number of pages15
JournalInternational Journal of Production Research
Volume56
Issue number14
DOIs
Publication statusPublished - 18 Jul 2018
Externally publishedYes

Keywords

  • integer programming
  • knowledge transfer
  • learning curves
  • productivity management
  • worker assignment

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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