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Distributed Heterogeneous Flow Shop Scheduling Method for Dual-Carbon Goals

调度(生产过程) 对偶(语法数字) 流水车间调度 计算机科学 分布式计算 作业车间调度 工程类 运营管理 嵌入式系统 布线(电子设计自动化) 文学类 艺术
作者
Xuesong Yan,Hao Zuo,Chengyu Hu,Wenyin Gong,Liang Gao
出处
期刊:IEEE Transactions on Automation Science and Engineering [Institute of Electrical and Electronics Engineers]
卷期号:: 1-12 被引量:2
标识
DOI:10.1109/tase.2024.3371940
摘要

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.
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