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Single machine scheduling with truncated job-dependent learning effect

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Abstract

In this paper we consider the single machine scheduling problem with truncated job-dependent learning effect. By the truncated job-dependent learning effect, we mean that the actual job processing time is a function which depends not only on the job-dependent learning effect (i.e., the learning in the production process of some jobs to be faster than that of others) but also on a control parameter. The objectives are to minimize the makespan, the total completion time, the total absolute deviation of completion time, the earliness, tardiness and common (slack) due-date penalty, respectively. Several polynomial time algorithms are proposed to optimally solve the problems with the above objective functions.

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Acknowledgments

The authors are grateful for the editor and two anonymous referees for their helpful comments on earlier version of the article. This research was supported by the National Natural Science Foundation of China (Grant No. 11001181), The Hong Kong Polytechnic University (PolyU’s project G-YL27) and the Program for Liaoning Excellent Talents in University (Grant No. LJQ2011014).

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Authors and Affiliations

  1. School of Science, Shenyang Aerospace University, Shenyang, 110136, China

    Xue-Ru Wang & Ji-Bo Wang

  2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

    Jian Jin & Ping Ji

Authors
  1. Xue-Ru Wang
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  2. Ji-Bo Wang
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  3. Jian Jin
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  4. Ping Ji
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Correspondence to Xue-Ru Wang.

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Wang, XR., Wang, JB., Jin, J. et al. Single machine scheduling with truncated job-dependent learning effect. Optim Lett 8, 669–677 (2014). https://doi.org/10.1007/s11590-012-0579-0

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  • DOI: https://doi.org/10.1007/s11590-012-0579-0

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