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Optimal flow-line conditions with worker variability

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  • Bentefouet, Frank
  • Nembhard, David A.

Abstract

When classical scheduling theory is applied to task sequencing problems, human characteristics are often not the primary consideration and their impact is often assumed away. In this paper, we examine the workforce scheduling problem in sequential flow line systems. We investigate the impact of within-worker and between-worker variability and discuss the selection of scheduling policies between fixed and work-sharing systems. The methodology includes human performance modeling with the objective to maximize throughput with general results with respect to productivity model. We are interested in determining and characterizing the optimal switching time between workers in work-sharing systems. A closed form solution is established in the case of two and three tasks with two workers. For a general number of workers and tasks we establish the maximum number of changes between assignments. We also establish a bound on throughput. Results allow one to solve workforce scheduling problems reduced complexity given the current set of bounds and conditions.

Suggested Citation

  • Bentefouet, Frank & Nembhard, David A., 2013. "Optimal flow-line conditions with worker variability," International Journal of Production Economics, Elsevier, vol. 141(2), pages 675-684.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:2:p:675-684
    DOI: 10.1016/j.ijpe.2012.10.008
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    References listed on IDEAS

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    Cited by:

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    2. Arvind Ashta, 2017. "Work-sharing from Different Angles: A literature review," Working Papers CEB 17-033, ULB -- Universite Libre de Bruxelles.
    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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    5. Hewitt, Mike & Chacosky, Austin & Grasman, Scott E. & Thomas, Barrett W., 2015. "Integer programming techniques for solving non-linear workforce planning models with learning," European Journal of Operational Research, Elsevier, vol. 242(3), pages 942-950.
    6. Anzanello, Michel J. & Fogliatto, Flavio S. & Santos, Luana, 2014. "Learning dependent job scheduling in mass customized scenarios considering ergonomic factors," International Journal of Production Economics, Elsevier, vol. 154(C), pages 136-145.

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