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Maximization of Labor Productivity Through Optimal Rest-Break Schedules

Author

Listed:
  • Stephen E. Bechtold

    (College of Business, Florida State University, Tallahassee, Florida 32306)

  • Ralph E. Janaro

    (Department of Management, Clarkson University, Potsdam, New York 13676)

  • De Witt L. Sumners

    (Department of Mathematics, Florida State University, Tallahassee, Florida 32306)

Abstract

This paper focuses upon the maximization of labor productivity through selection of the optimal number, duration, and placement of rest breaks. It is demonstrated that resolution of the limited validity associated with previous models leads to a mixed-integer quadratic programming formulation of this problem. Efficient analytic procedures are developed which allow solutions to be obtained in minutes using a hand-held calculator. Then, the characteristics, properties, and potential effectiveness of optimal solutions are examined through an illustration from an existing work environment. Issues relevant to the implementation of the models are presented in a summary of an initial experiment in which one of the models in this paper was used to select rest break policy. The results of this experiment indicated that the output achieved using the model's policy was 13% higher than the output achieved when the subjects used their own rest break policies. Several future research topics are also identified.

Suggested Citation

  • Stephen E. Bechtold & Ralph E. Janaro & De Witt L. Sumners, 1984. "Maximization of Labor Productivity Through Optimal Rest-Break Schedules," Management Science, INFORMS, vol. 30(12), pages 1442-1458, December.
    • Handle: RePEc:inm:ormnsc:v:30:y:1984:i:12:p:1442-1458
    DOI: 10.1287/mnsc.30.12.1442
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    Citations

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    Cited by:

    1. Janiak, Adam & Kovalyov, Mikhail Y., 2006. "Scheduling in a contaminated area: A model and polynomial algorithms," European Journal of Operational Research, Elsevier, vol. 173(1), pages 125-132, August.
    2. A Janiak & M Y Kovalyov, 2008. "Scheduling jobs in a contaminated area: a model and heuristic algorithms," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 977-987, July.
    3. André Scholz & Johannes Wendsche & Argang Ghadiri & Usha Singh & Theo Peters & Stefan Schneider, 2019. "Methods in Experimental Work Break Research: A Scoping Review," IJERPH, MDPI, vol. 16(20), pages 1-23, October.
    4. Xu, Shuling & Hall, Nicholas G., 2021. "Fatigue, personnel scheduling and operations: Review and research opportunities," European Journal of Operational Research, Elsevier, vol. 295(3), pages 807-822.
    5. Manel Baucells & Lin Zhao, 2019. "It Is Time to Get Some Rest," Management Science, INFORMS, vol. 65(4), pages 1717-1734, April.
    6. Svend Erik Mathiassen & David M Hallman & Eugene Lyskov & Staffan Hygge, 2014. "Can Cognitive Activities during Breaks in Repetitive Manual Work Accelerate Recovery from Fatigue? A Controlled Experiment," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-11, November.
    7. Thompson, Gary M. & Pullman, Madeleine E., 2007. "Scheduling workforce relief breaks in advance versus in real-time," European Journal of Operational Research, Elsevier, vol. 181(1), pages 139-155, August.
    8. Thompson, Gary M. & Goodale, John C., 2006. "Variable employee productivity in workforce scheduling," European Journal of Operational Research, Elsevier, vol. 170(2), pages 376-390, April.
    9. Sheng Yu, 2015. "An optimal single-machine scheduling with linear deterioration rate and rate-modifying activities," Journal of Combinatorial Optimization, Springer, vol. 30(2), pages 242-252, August.

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