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Worst-case behavior of simple sequencing rules in flow shop scheduling with general position-dependent learning effects

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  • Ji-Bo Wang
  • Ming-Zheng Wang

Abstract

A real industrial production phenomenon, referred to as learning effects, has drawn increasing attention. However, most research on this issue considers only single machine problems. Motivated by this limitation, this paper considers flow shop scheduling problems with a general position-dependent learning effects. By the general position-dependent learning effects, we mean that the actual processing time of a job is defined by a general non-increasing function of its scheduled position. The objective is to minimize one of the five regular performance criteria, namely, the total completion time, the makespan, the total weighted completion time, the total weighted discounted completion time, and the sum of the quadratic job completion times. We present heuristic algorithms by using the optimal permutations for the corresponding single machine scheduling problems. We also analyze the worst-case bound of our heuristic algorithms. Copyright Springer Science+Business Media, LLC 2011

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  • Ji-Bo Wang & Ming-Zheng Wang, 2011. "Worst-case behavior of simple sequencing rules in flow shop scheduling with general position-dependent learning effects," Annals of Operations Research, Springer, vol. 191(1), pages 155-169, November.
    • Handle: RePEc:spr:annopr:v:191:y:2011:i:1:p:155-169:10.1007/s10479-011-0923-2
    DOI: 10.1007/s10479-011-0923-2
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    Cited by:

    1. Xinyu Sun & Xin-Na Geng & Tao Liu, 2020. "Due-window assignment scheduling in the proportionate flow shop setting," Annals of Operations Research, Springer, vol. 292(1), pages 113-131, September.
    2. Jin Qian & Haiyan Han, 2022. "Improved algorithms for proportionate flow shop scheduling with due-window assignment," Annals of Operations Research, Springer, vol. 309(1), pages 249-258, February.
    3. Li, Gang & Wang, Xiao-Yuan & Wang, Ji-Bo & Sun, Lin-Yan, 2013. "Worst case analysis of flow shop scheduling problems with a time-dependent learning effect," International Journal of Production Economics, Elsevier, vol. 142(1), pages 98-104.
    4. 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.
    5. Lin-Hui Sun & Kai Cui & Ju-Hong Chen & Jun Wang & Xian-Chen He, 2013. "Some results of the worst-case analysis for flow shop scheduling with a learning effect," Annals of Operations Research, Springer, vol. 211(1), pages 481-490, December.
    6. Adam Janiak & Mikhail Kovalyov & Maciej Lichtenstein, 2013. "Strong NP-hardness of scheduling problems with learning or aging effect," Annals of Operations Research, Springer, vol. 206(1), pages 577-583, July.
    7. Radosław Rudek, 2012. "Scheduling problems with position dependent job processing times: computational complexity results," Annals of Operations Research, Springer, vol. 196(1), pages 491-516, July.
    8. Lin-Hui Sun & Kai Cui & Ju-Hong Chen & Jun Wang & Xian-Chen He, 2013. "Research on permutation flow shop scheduling problems with general position-dependent learning effects," Annals of Operations Research, Springer, vol. 211(1), pages 473-480, December.
    9. Lu Liu & Jian-Jun Wang & Xiao-Yuan Wang, 2016. "Single machine due-window assignment scheduling with resource-dependent processing times to minimise total resource consumption cost," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1186-1195, February.

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