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

Author

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  • D A Nembhard

    (The Pennsylvania State University, University Park)

  • N Osothsilp

    (Chulalongkorn University)

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.

Suggested Citation

  • D A Nembhard & N Osothsilp, 2005. "Learning and forgetting-based worker selection for tasks of varying complexity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 576-587, May.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:5:d:10.1057_palgrave.jors.2601842
    DOI: 10.1057/palgrave.jors.2601842
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    References listed on IDEAS

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    1. Scott M. Shafer & David A. Nembhard & Mustafa V. Uzumeri, 2001. "The Effects of Worker Learning, Forgetting, and Heterogeneity on Assembly Line Productivity," Management Science, INFORMS, vol. 47(12), pages 1639-1653, December.
    2. Kher, Hemant V. & Malhotra, Manoj K. & Philipoom, Patrick R. & Fry, Timothy D., 1999. "Modeling simultaneous worker learning and forgetting in dual resource constrained systems," European Journal of Operational Research, Elsevier, vol. 115(1), pages 158-172, May.
    3. Kher, HV & Malhotra, MK, 1994. "Acquiring and operationalizing worker flexibility in dual resource constrained job shops with worker transfer delays and learning losses," Omega, Elsevier, vol. 22(5), pages 521-533, September.
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    Cited by:

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    2. Chen, Xi & Hewitt, Mike & Thomas, Barrett W., 2018. "An approximate dynamic programming method for the multi-period technician scheduling problem with experience-based service times and stochastic customers," International Journal of Production Economics, Elsevier, vol. 196(C), pages 122-134.
    3. Valeva, Silviya & Hewitt, Mike & Thomas, Barrett W. & Brown, Kenneth G., 2017. "Balancing flexibility and inventory in workforce planning with learning," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 194-207.

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