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A note on a makespan minimization problem with a multi-ability learning effect

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  • Janiak, Adam
  • Rudek, RadosLaw
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    Abstract

    In the scheduling literature the learning effect is perceived as a process of acquiring experience by a processor (e.g. a human worker) in one ability. However, in many real-life problems the processor, during execution of jobs, increases its experience in different, very often independent, abilities (skills). In consequence, it causes the overall growth of the efficiency of the processor. According to this observation, in this paper, we bring into scheduling a new approach called multi-ability learning that generalizes the existing ones and models more precisely real-life settings. On this basis, we focus on a makespan minimization problem with the proposed learning model and provide optimal polynomial time algorithms for its special cases, which often occur in management.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 38 (2010)
    Issue (Month): 3-4 (June)
    Pages: 213-217

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    Handle: RePEc:eee:jomega:v:38:y:2010:i:3-4:p:213-217

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    Keywords: Scheduling Learning effect Single machine Computational complexity;

    References

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    1. Chua, Ai Ling & Pan, Shan L., 2008. "Knowledge transfer and organizational learning in IS offshore sourcing," Omega, Elsevier, vol. 36(2), pages 267-281, April.
    2. Fosfuri, Andrea & Tribø, Josep A., 2008. "Exploring the antecedents of potential absorptive capacity and its impact on innovation performance," Omega, Elsevier, vol. 36(2), pages 173-187, April.
    3. Chen, Jen-Shiang, 2008. "Optimization models for the tool change scheduling problem," Omega, Elsevier, vol. 36(5), pages 888-894, October.
    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. Mosheiov, Gur & Sidney, Jeffrey B., 2003. "Scheduling with general job-dependent learning curves," European Journal of Operational Research, Elsevier, vol. 147(3), pages 665-670, June.
    6. Potts, Chris N. & Kovalyov, Mikhail Y., 2000. "Scheduling with batching: A review," European Journal of Operational Research, Elsevier, vol. 120(2), pages 228-249, January.
    7. Wang, Ji-Bo, 2007. "Single-machine scheduling problems with the effects of learning and deterioration," Omega, Elsevier, vol. 35(4), pages 397-402, August.
    8. 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.
    9. Hsu, Chin-Chun & Pereira, Arun, 2008. "Internationalization and performance: The moderating effects of organizational learning," Omega, Elsevier, vol. 36(2), pages 188-205, April.
    10. Mosheiov, Gur, 2001. "Scheduling problems with a learning effect," European Journal of Operational Research, Elsevier, vol. 132(3), pages 687-693, August.
    11. 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.
    12. Wang, F. -K. & Lee, W., 2001. "Learning curve analysis in total productive maintenance," Omega, Elsevier, vol. 29(6), pages 491-499, December.
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    Cited by:
    1. Sterna, Malgorzata, 2011. "A survey of scheduling problems with late work criteria," Omega, Elsevier, vol. 39(2), pages 120-129, April.
    2. Sáenz-Royo, Carlos & Salas-Fumás, Vicente, 2013. "Learning to learn and productivity growth: Evidence from a new car-assembly plant," Omega, Elsevier, vol. 41(2), pages 336-344.

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