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Single-machine scheduling problems with general truncated sum-of-actual-processing-time-based learning effect

Author

Listed:
  • Zhongyi Jiang

    (Changzhou University)

  • Fangfang Chen

    (Changzhou University)

  • Xiandong Zhang

    (Fudan University)

Abstract

We study single machine scheduling problems with general truncated sum-of-actual-processing-time-based learning effect. In the general truncated learning model, the actual processing time of a job is affected by the sum of actual processing times of previous jobs and by a job-dependent truncation parameter. We show that the single machine problems to minimize makespan and to minimize the sum of weighted completion times are both at least ordinary NP-hard and the single machine problem to minimize maximum lateness is strongly NP-hard. We then show polynomial solvable cases and approximation algorithms for these problems. Computational experiments are also conducted to show the effectiveness of our approximation algorithms.

Suggested Citation

  • Zhongyi Jiang & Fangfang Chen & Xiandong Zhang, 2022. "Single-machine scheduling problems with general truncated sum-of-actual-processing-time-based learning effect," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 116-139, January.
    • Handle: RePEc:spr:jcomop:v:43:y:2022:i:1:d:10.1007_s10878-021-00752-y
    DOI: 10.1007/s10878-021-00752-y
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    References listed on IDEAS

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