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Single-Machine Scheduling with Learning Effects and Maintenance: A Methodological Note on Some Polynomial-Time Solvable Cases

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  • Kuo-Ching Ying
  • Chung-Cheng Lu
  • Shih-Wei Lin
  • Jie-Ning Chen

Abstract

This work addresses four single-machine scheduling problems (SMSPs) with learning effects and variable maintenance activity. The processing times of the jobs are simultaneously determined by a decreasing function of their corresponding scheduled positions and the sum of the processing times of the already processed jobs. Maintenance activity must start before a deadline and its duration increases with the starting time of the maintenance activity. This work proposes a polynomial-time algorithm for optimally solving two SMSPs to minimize the total completion time and the total tardiness with a common due date.

Suggested Citation

  • Kuo-Ching Ying & Chung-Cheng Lu & Shih-Wei Lin & Jie-Ning Chen, 2017. "Single-Machine Scheduling with Learning Effects and Maintenance: A Methodological Note on Some Polynomial-Time Solvable Cases," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-6, August.
    • Handle: RePEc:hin:jnlmpe:7532174
    DOI: 10.1155/2017/7532174
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