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Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times

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Abstract

This paper proposes a single machine scheduling problem with learning-effect and release times by considering two objectives requiring minimization of makespan and total tardiness, simultaneously. Due to the NP-hardness of this problem, two memetic algorithms with meme variants are presented for solving the bi-objective problem and applied by combining three different scalarization methods, including weighted sum, conic, and tchebycheff. The performance of all memetic algorithms with the meme is investigated across randomly generated twenty-seven test problems ranging from ‘small’ to ‘large’ size. The experimental results indicate that the Multimeme Memetic Algorithm using the tchebycheff outperforms the other algorithms producing the best-known results for almost all bi-objective single machine scheduling instances with learning-effects. All algorithms perform effectively in solving large-sized problems with up to 200 jobs.

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Acknowledgements

The authors would like to thank the Department of Computer Science at Yalova University for the laboratory support in conducting large-sized experiments. Authors wish to acknowledge the anonymous referees for their comments to improve the presentation of this paper.

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Deliktaş, D. Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times. Flex Serv Manuf J 34, 748–784 (2022). https://doi.org/10.1007/s10696-021-09434-7

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