Distributed Heterogeneous Flow Shop Scheduling Method for Dual-Carbon Goals

As an important field leading the rapid development of China’s economy, industry is an important support for building a modern power, and it is also a large carbon emitter in China. Therefore, it is of key significance to promote industry to achieve the peak of carbon emissions for the realization of China’s “dual-carbon goals”. Aiming at the problem of distributed heterogeneous flow shop scheduling problem based on dual-carbon goals (DHFSP-DCGs), a novel distributed heterogeneous flow shop scheduling model was constructed to minimize the maximum completion time and total carbon emissions, and a knowledge-driven multi-objective memetic algorithm was proposed. Firstly, considering the machine characteristics of heterogeneous factories and the conflict between two optimization objectives, the encoding and decoding methods based on double sequences are designed. Secondly, a cooperative initialization strategy is proposed to generate the initial solutions with good diversity and convergence. Thirdly, according to the characteristics of distributed heterogeneous flow shop scheduling problem, a knowledge-based local search strategy is designed to improve the quality of the solution and the performance of the algorithm, and carbon reduction strategy is used to reduce the carbon emission in the production scheduling process. Finally, the effectiveness of the proposed strategy and algorithm is verified by comparative experiments. Note to Practitioners—This paper is to solve the problem of distributed heterogeneous flow shop scheduling problem based on dual-carbon goals (DHFSP-DCGs), and the methods proposed could bring many benefits to practitioners. Firstly, considering the machine characteristics of heterogeneous factories and the conflict between two optimization objectives, the encoding and decoding methods based on double sequences are designed. Secondly, a cooperative initialization strategy is proposed to generate the initial solutions with good diversity and convergence. Thirdly, according to the characteristics of distributed heterogeneous flow shop scheduling problem, a knowledge-based local search strategy is designed to improve the quality of the solution and the performance of the algorithm, and carbon reduction strategy is used to reduce the carbon emission in the production scheduling process.

• Publication year

  2025

• Venue

  IEEE Transactions on Automation Science and Engineering

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1109/TASE.2024.3371940
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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