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Single-Machine Scheduling with Accelerating Learning Effects

Author

Listed:
  • T. C. E. Cheng
  • Shih-Chang Tseng
  • Peng-Jen Lai
  • Wen-Chiung Lee

Abstract

Scheduling with learning effects has been widely studied. However, there are situations where the learning effect might accelerate. In this paper, we propose a new model where the learning effect accelerates as time goes by. We derive the optimal solutions for the single-machine problems to minimize the makespan, total completion time, total weighted completion time, maximum lateness, maximum tardiness, and total tardiness.

Suggested Citation

  • T. C. E. Cheng & Shih-Chang Tseng & Peng-Jen Lai & Wen-Chiung Lee, 2013. "Single-Machine Scheduling with Accelerating Learning Effects," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, November.
  • Handle: RePEc:hin:jnlmpe:816235
    DOI: 10.1155/2013/816235
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    Cited by:

    1. B. C. Giri & M. Masanta, 2022. "A closed-loop supply chain model with uncertain return and learning-forgetting effect in production under consignment stock policy," Operational Research, Springer, vol. 22(2), pages 947-975, April.
    2. Derya Deliktaş, 2022. "Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 748-784, September.
    3. Heuser, Patricia & Tauer, Björn, 2023. "Single-machine scheduling with product category-based learning and forgetting effects," Omega, Elsevier, vol. 115(C).

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