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Stochastic scheduling problems with general position-based learning effects and stochastic breakdowns

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Abstract

The focus of this study is to analyze position-based learning effects in single-machine stochastic scheduling problems. The optimal permutation policies for the stochastic scheduling problems with and without machine breakdowns are examined, where the performance measures are the expectation and variance of the makespan, the expected total completion time, the expected total weighted completion time, the expected weighted sum of the discounted completion times, the maximum lateness and the maximum tardiness.

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Acknowledgments

This research was partially supported by the Natural Science Foundation of China under Grant No. 71071056, and the Australian Research Council Discovery Project Grant No. DP1094153.

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Authors and Affiliations

  1. Department of Mathematics, Jiangxi Normal University, Nanchang, China

    Yebin Zhang

  2. Department of Statistics and Actuarial Science, East China Normal University, Shanghai, China

    Xianyi Wu

  3. Department of Applied Finance and Actuarial Studies Macquarie University, Sydney, Australia

    Xianyi Wu & Xian Zhou

Authors
  1. Yebin Zhang
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  2. Xianyi Wu
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  3. Xian Zhou
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Correspondence to Xianyi Wu.

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Zhang, Y., Wu, X. & Zhou, X. Stochastic scheduling problems with general position-based learning effects and stochastic breakdowns. J Sched 16, 331–336 (2013). https://doi.org/10.1007/s10951-012-0306-9

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  • DOI: https://doi.org/10.1007/s10951-012-0306-9

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