Journal article
Learning and forgetting-based worker selection for tasks of varying complexity
The Journal of the Operational Research Society, Vol.56(5), pp.576-587
05/01/2005
DOI: 10.1057/palgrave.jors.2601842
Abstract
This paper presents an approach for selecting workers for tasks of varying complexity based on individual learning and forgetting characteristics in order to improve system productivity. The performance of a learning and forgetting-based selection (LFBS) policy is examined using simulation and compared to a baseline policy representing criteria used in practice. The effects of factors including worker redundancy and task-tenure on productivity are also examined in the environment of continuously staffed independent tasks. Results demonstrate that the LFBs policy significantly improves productivity relative to common practice and suggests that lower levels of redundancy and shorter task-tenures tend to mitigate some of the negative effects of forgetting.
Details
- Title: Subtitle
- Learning and forgetting-based worker selection for tasks of varying complexity
- Creators
- D A Nembhard - Pennsylvania State UniversityN Osothsilp - Chulalongkorn University
- Resource Type
- Journal article
- Publication Details
- The Journal of the Operational Research Society, Vol.56(5), pp.576-587
- Publisher
- Taylor & Francis
- DOI
- 10.1057/palgrave.jors.2601842
- ISSN
- 0160-5682
- eISSN
- 1476-9360
- Language
- English
- Date published
- 05/01/2005
- Academic Unit
- Business Analytics; Industrial and Systems Engineering
- Record Identifier
- 9984186980402771
Metrics
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