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Scheduling for trial production with a parallel machine and multitasking scheduling model

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Abstract

Trial production is essential for some manufacturing systems to adjust production lines before mass production. Scheduling for trial production promotes carrying out mass production activities faster. In this paper, a set of job families with setup time should be processed on several identical parallel machines, in which jobs belonging to the same family are processed according to multitasking scheduling. The objective is to minimize the total weighted completion time. Structural properties of the investigated problem are proposed. Subsequently, a novel branch-and-price algorithm (B &P) is proposed. In detail, a label-setting algorithm is presented for fast solving the pricing problem of B &P, and the structural properties are utilized to speed up the algorithm. Computational results show that the proposed B &P has an excellent performance in both optimality and efficiency. Within the limit time of 1800 seconds, B &P is able to optimally solve 100.00% of small-size problems and 91.11% of large-size problems. The average gap of B &P in percentage between the upper bound and theoretical lower bound is close to 0.00%. Results of the final case study demonstrate that, in general, the proposed algorithm improves the efficiency of the trial production plan.

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Data Availability

The data that support the findings of this study are available from the website at https://github.com/jinshenggao/Applied_Intelligent_review.git.

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Acknowledgements

This work was supported by the Beijing Natural Science Foundation[grant number L201003]; the National Natural Science Foundation of China [grant number 62173025]; and the Key Research and Development Project of Guangdong Province [grant number 2021B0101420003].

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Authors and Affiliations

  1. School of Economics and Management, North China Electric Power University, No.2 Beinong Road, Huilongguan Town, Changping District, Beijing, 102206, Beijing, China

    Jinsheng Gao

  2. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, No.3 Shangyuancun Haidian District, Beijing, 100044, Beijing, China

    Xiaomin Zhu

  3. School of Economics and Management, Beijing Jiaotong University, No.3 Shangyuancun Haidian District, Beijing, 100044, Beijing, China

    Runtong Zhang

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Correspondence to Xiaomin Zhu.

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Xiaomin Zhu and Runtong Zhang contributed equally to this work.

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Gao, J., Zhu, X. & Zhang, R. Scheduling for trial production with a parallel machine and multitasking scheduling model. Appl Intell 53, 26907–26926 (2023). https://doi.org/10.1007/s10489-023-04845-5

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