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Single-machine scheduling with general sum-of-processing-time-based and position-based learning effects

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  • Lai, Peng-Jen
  • Lee, Wen-Chiung
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    Abstract

    In this paper, we propose a new learning effect model in which the actual job processing time is a general function of the normal processing time of jobs already processed and its scheduled position. This model has the advantage that different learning curves can be constructed easily, such as the plateau function. It is found that most of the models in the literature are special cases of our proposed model. The optimal sequences for some single-machine problems are then provided.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 39 (2011)
    Issue (Month): 5 (October)
    Pages: 467-471

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    Handle: RePEc:eee:jomega:v:39:y:2011:i:5:p:467-471

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    Related research

    Keywords: Scheduling Learning effect Single-machine;

    References

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    1. Mosheiov, Gur, 2001. "Scheduling problems with a learning effect," European Journal of Operational Research, Elsevier, vol. 132(3), pages 687-693, August.
    2. Koulamas, Christos & Kyparisis, George J., 2007. "Single-machine and two-machine flowshop scheduling with general learning functions," European Journal of Operational Research, Elsevier, vol. 178(2), pages 402-407, April.
    3. Wang, Ji-Bo, 2007. "Single-machine scheduling problems with the effects of learning and deterioration," Omega, Elsevier, vol. 35(4), pages 397-402, August.
    4. Chen, Wen-Jinn, 2009. "Minimizing number of tardy jobs on a single machine subject to periodic maintenance," Omega, Elsevier, vol. 37(3), pages 591-599, June.
    5. Biskup, Dirk, 2008. "A state-of-the-art review on scheduling with learning effects," European Journal of Operational Research, Elsevier, vol. 188(2), pages 315-329, July.
    6. Reddy Dondeti, V. & Mohanty, Bidhu B., 1998. "Impact of learning and fatigue factors on single machine scheduling with penalties for tardy jobs," European Journal of Operational Research, Elsevier, vol. 105(3), pages 509-524, March.
    7. Vallada, Eva & Ruiz, Rubén, 2010. "Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem," Omega, Elsevier, vol. 38(1-2), pages 57-67, February.
    8. Ribas, Imma & Companys, Ramon & Tort-Martorell, Xavier, 2011. "An iterated greedy algorithm for the flowshop scheduling problem with blocking," Omega, Elsevier, vol. 39(3), pages 293-301, June.
    9. Lee, Wen-Chiung & Wu, Chin-Chia & Hsu, Peng-Hsiang, 2010. "A single-machine learning effect scheduling problem with release times," Omega, Elsevier, vol. 38(1-2), pages 3-11, February.
    10. Sterna, Malgorzata, 2011. "A survey of scheduling problems with late work criteria," Omega, Elsevier, vol. 39(2), pages 120-129, April.
    11. Kuo, Wen-Hung & Yang, Dar-Li, 2006. "Minimizing the total completion time in a single-machine scheduling problem with a time-dependent learning effect," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1184-1190, October.
    12. Michael A. Lapré & Amit Shankar Mukherjee & Luk N. Van Wassenhove, 2000. "Behind the Learning Curve: Linking Learning Activities to Waste Reduction," Management Science, INFORMS, vol. 46(5), pages 597-611, May.
    13. Hsu, Chin-Chun & Pereira, Arun, 2008. "Internationalization and performance: The moderating effects of organizational learning," Omega, Elsevier, vol. 36(2), pages 188-205, April.
    14. Day, Jamison M. & Daniel Wright, P. & Schoenherr, Tobias & Venkataramanan, Munirpallam & Gaudette, Kevin, 2009. "Improving routing and scheduling decisions at a distributor of industrial gasses," Omega, Elsevier, vol. 37(1), pages 227-237, February.
    15. Biskup, Dirk, 1999. "Single-machine scheduling with learning considerations," European Journal of Operational Research, Elsevier, vol. 115(1), pages 173-178, May.
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