Learning and forgetting-based worker selection for tasks of varying complexity

D A Nembhard () and N Osothsilp
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D A Nembhard: The Pennsylvania State University, University Park
N Osothsilp: Chulalongkorn University

Journal of the Operational Research Society, 2005, vol. 56, issue 5, 576-587

Abstract: 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.

Keywords: learning; forgetting; allocation; production; manpower planning; scheduling; simulation; behaviour (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (6)

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DOI: 10.1057/palgrave.jors.2601842

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