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Flow-shop scheduling with a learning effect

Author

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  • J-B Wang

    (Shenyang Institute of Aeronautical Engineering
    Dalian University of Technology)

  • Z-Q Xia

    (Dalian University of Technology)

Abstract

The paper is devoted to some flow-shop scheduling problems with a learning effect. The objective is to minimize one of the two regular performance criteria, namely, makespan and total flowtime. A heuristic algorithm with worst-case bound m for each criteria is given, where m is the number of machines. Furthermore, a polynomial algorithm is proposed for both of the special cases: identical processing time on each machine and an increasing series of dominating machines. An example is also constructed to show that the classical Johnson's rule is not the optimal solution for the two-machine flow-shop scheduling to minimize makespan with a learning effect. Some extensions of the problem are also shown.

Suggested Citation

  • J-B Wang & Z-Q Xia, 2005. "Flow-shop scheduling with a learning effect," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(11), pages 1325-1330, November.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:11:d:10.1057_palgrave.jors.2601856
    DOI: 10.1057/palgrave.jors.2601856
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Lee, Wen-Chiung & Chung, Yu-Hsiang, 2013. "Permutation flowshop scheduling to minimize the total tardiness with learning effects," International Journal of Production Economics, Elsevier, vol. 141(1), pages 327-334.
    2. J-B Wang, 2010. "Single-machine scheduling with a sum-of-actual-processing-time-based learning effect," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 172-177, January.
    3. Wu, Chin-Chia & Lee, Wen-Chiung, 2009. "A note on the total completion time problem in a permutation flowshop with a learning effect," European Journal of Operational Research, Elsevier, vol. 192(1), pages 343-347, January.
    4. Wang, J.-B. & Ng, C.T. & Cheng, T.C.E. & Liu, L.L., 2008. "Single-machine scheduling with a time-dependent learning effect," International Journal of Production Economics, Elsevier, vol. 111(2), pages 802-811, February.
    5. Xu, Zhiyong & Sun, Linyan & Gong, Juntao, 2008. "Worst-case analysis for flow shop scheduling with a learning effect," International Journal of Production Economics, Elsevier, vol. 113(2), pages 748-753, June.
    6. Bai, Danyu & Tang, Mengqian & Zhang, Zhi-Hai & Santibanez-Gonzalez, Ernesto DR, 2018. "Flow shop learning effect scheduling problem with release dates," Omega, Elsevier, vol. 78(C), pages 21-38.
    7. 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.
    8. Janiak, Adam & Janiak, Władysław A. & Krysiak, Tomasz & Kwiatkowski, Tomasz, 2015. "A survey on scheduling problems with due windows," European Journal of Operational Research, Elsevier, vol. 242(2), pages 347-357.
    9. Qian, Jin & Lin, Hexiang & Kong, Yufeng & Wang, Yuansong, 2020. "Tri-criteria single machine scheduling model with release times and learning factor," Applied Mathematics and Computation, Elsevier, vol. 387(C).
    10. Wu, Cedric Hsi-Jui & Liang, Rong-Da, 2011. "The relationship between white-water rafting experience formation and customer reaction: a flow theory perspective," Tourism Management, Elsevier, vol. 32(2), pages 317-325.
    11. Helin, Jenny & Avenier, Marie-José, 2016. "Inquiring into arresting moments over time: Towards an understanding of stability within change," Scandinavian Journal of Management, Elsevier, vol. 32(3), pages 142-149.
    12. 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.
    13. Gordon, Valery S. & Strusevich, Vitaly A., 2009. "Single machine scheduling and due date assignment with positionally dependent processing times," European Journal of Operational Research, Elsevier, vol. 198(1), pages 57-62, October.

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