Abstract
This paper addresses single-machine scheduling problems with truncated learning effects. The objective is to determine the optimal job schedule such that the makespan, the total weighted completion time and the maximum lateness are to be minimized. All the considered problems are NP-hard; hence, for each problem, we propose the heuristic and branch-and-bound algorithms. Extensive numerical experiments validate the efficiency of the proposed solution algorithms on a set of randomly generated instances.
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Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
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Acknowledgements
This work was supported by LiaoNing Revitalization Talents Program (XLYC2002017). This work was also supported by the National Natural Science Foundation of China (71971165) and the National Key Research and Development Program of China (2021YFB3301801).
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Communicated by Hector Cancela.
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Wang, JB., Zhang, LH., Lv, ZG. et al. Heuristic and exact algorithms for single-machine scheduling problems with general truncated learning effects. Comp. Appl. Math. 41, 417 (2022). https://doi.org/10.1007/s40314-022-02133-5
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DOI: https://doi.org/10.1007/s40314-022-02133-5