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Scheduling with a position-weighted learning effect

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Abstract

In general, human learning takes time to build up, which results from a worker gaining experience from repeating similar operations over time. In the early stage of processing a given set of similar jobs, a worker is not familiar with the operations, so his learning effect on the jobs scheduled early is not apparent. On the other hand, when the worker has gained experience in processing the jobs his learning improves. So a worker’s learning effect on a job depends not only on the total processing time of the jobs that he has processed but also on the job position. In this paper we introduce a position-weighted learning effect model for scheduling problems. We provide optimal solutions for the single-machine problems to minimize the makespan and the total completion time, and an optimal solution for the single-machine problem to minimize the total tardiness under an agreeable situation. We also consider two special cases of the flowshop problem.

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Acknowledgments

We are grateful to two anonymous referees for their helpful comments on earlier versions of our paper. This research was supported in part by the National Science Council of Taiwan, Republic of China, under grant number NSC-98-2221-E-150-032. Professor Yang is currently a Fulbright visiting scholar to Oregon State University and he was supported in part by the National Science Council of Taiwan, Republic of China, under grant number NSC-99-2221-E-150-034-MY2.

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Authors and Affiliations

  1. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    T. C. E. Cheng

  2. Department of Information Management, National Formosa University, Yun-Lin, 632, Taiwan, ROC

    Wen-Hung Kuo & Dar-Li Yang

Authors
  1. T. C. E. Cheng
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  2. Wen-Hung Kuo
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  3. Dar-Li Yang
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Correspondence to Wen-Hung Kuo.

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Cheng, T.C.E., Kuo, WH. & Yang, DL. Scheduling with a position-weighted learning effect. Optim Lett 8, 293–306 (2014). https://doi.org/10.1007/s11590-012-0574-5

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  • DOI: https://doi.org/10.1007/s11590-012-0574-5

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