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Energy-efficient distributed heterogeneous blocking flowshop scheduling problem using a knowledge-based iterated Pareto greedy algorithm

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Abstract

In recent years, both distributed scheduling and energy-efficient scheduling have attracted great attention in production systems. This paper studies an energy-efficient distributed blocking flowshop scheduling problem where several heterogeneous factories cooperate to process jobs. A knowledge-based iterated Pareto greedy algorithm (KBIPG) is proposed to minimize simultaneously the makespan and total energy consumption. Based on a speed scaling framework that allows machines to process different jobs at different speed levels or remain in the standby mode, the KBIPG has two stages, where the difference lies in whether to adjust the processing speed. First, two multi-objective insertion procedures are proposed to form construction procedures. Second, we presented an efficient destruction procedure for each stage separately. Third, two local intensification methods are designed based on adjusting machine speeds, including the energy-saving procedure that optimizes the total energy consumption and the speedup-based local search procedure that optimizes the makespan. The KBIPG algorithm starts with generating solutions under various initial machine speed matrixes with different levels and then goes through a two-stage loop based on the proposed procedures. Computational experiments and comparisons with five algorithms demonstrate the effectiveness of the proposed KBIPG algorithm.

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Data availability

The data generated and analyzed during this study are available in the GitHub repository: https://github.com/ShuaiChen-lgtm/NCAA_Data.git.

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Acknowledgements

This research is partially supported by the National Key Research and Development Program 2020YFB1708200, the National Natural Science Fund for Distinguished Young Scholars of China under Grant 51825502, the National Science Foundation of China 62273221 and 61973203, the Program of Shanghai Academic/Technolgical Research Learder 21XD1401000 and Shanghai Key Laboratory of Power station Automation Technology.

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, People’s Republic of China

    Shuai Chen, Quan-Ke Pan, Zhong-Hua Miao & Chen Peng

  2. School of Computer Science, Liaocheng University, Liaocheng, 252000, People’s Republic of China

    Quan-Ke Pan

  3. State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China

    Liang Gao

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Correspondence to Quan-Ke Pan.

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Chen, S., Pan, QK., Gao, L. et al. Energy-efficient distributed heterogeneous blocking flowshop scheduling problem using a knowledge-based iterated Pareto greedy algorithm. Neural Comput & Applic 35, 6361–6381 (2023). https://doi.org/10.1007/s00521-022-08012-8

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