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Research on permutation flow shop scheduling problems with general position-dependent learning effects

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Abstract

Machine learning exists in many realistic scheduling situations. This study focuses on permutation flow shop scheduling problems, where the actual processing time of a job is defined by a general non-increasing function of its scheduled position, i.e., general position-dependent learning effects. The objective functions are to minimize the total completion time, the makespan, the total weighted completion time, and the total weighted discounted completion time, respectively. To solve these problems, we present approximation algorithms based on the optimal permutations for the corresponding single machine scheduling problems and analyze their worst-case error bound.

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Acknowledgements

We are grateful to two anonymous referees for their helpful comments on an earlier version of this paper. This research was supported by the open project of The State Key Laboratory for Manufacturing Systems Engineering (Grant no. sklms2012009), the Program for Shanxi Natural science foundation research (Grant no. 2012JQ9006) and the National Natural Science Foundation of China (Grant No. 71272117).

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Correspondence to Lin-Hui Sun.

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Sun, LH., Cui, K., Chen, JH. et al. Research on permutation flow shop scheduling problems with general position-dependent learning effects. Ann Oper Res 211, 473–480 (2013). https://doi.org/10.1007/s10479-013-1481-6

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