中国机械工程

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基于改进人工蜂群算法的多目标绿色柔性作业车间调度研究

李益兵1,2;黄炜星1;吴锐1   

  1. 1.武汉理工大学机电工程学院,武汉,430070
    2.数字制造湖北省重点实验室,武汉,430070
  • 出版日期:2020-06-10 发布日期:2020-07-03
  • 基金资助:
    国家自然科学基金资助项目(51705384,51875430)

Research on Multi-objective Green Flexible Job-shop Scheduling Based on Improved ABC Algorithm

LI Yibing1,2;HUANG Weixing1;WU Rui1   

  1. 1.School of Mechanicaland Electronic Engineering,Wuhan University of Technology,Wuhan,430070
    2.Hubei Key Laboratory of Digital Manufacturing,Wuhan University of Technology,Wuhan,430070
  • Online:2020-06-10 Published:2020-07-03

摘要: 针对多目标绿色柔性作业车间调度问题(MGFJSP)的特点,提出从碳排放量、噪声和废弃物这3个指标来综合评定环境污染程度,建立了以最小化最大完成时间和环境污染程度为优化目标的MGFJSP模型,并提出了一种改进的人工蜂群算法来求解该模型。算法的具体改进包括:设计了一种三维向量的编码和对应解码方案,在跟随蜂搜索阶段引入一种有效的动态邻域搜索操作来提高算法的局部搜索能力,在侦查蜂阶段提出产生新食物源的策略用于增加种群的多样性。最后进行了实验研究与算法对比,以验证所建模型和所提算法的有效性。

关键词: 绿色柔性作业车间调度, 多目标优化, 环境污染, 人工蜂群算法

Abstract: Aiming at the characteristics of multi-objective green flexible job-shop scheduling problem (MGFJSP), three indicators of carbon emissions, noises and wastes were proposed to evaluate the degree of environmental pollution comprehensively. A MGFJSP model was established with the optimization goals of minimizing the makespan and the degree of environmental pollution. And the improved ABC algorithm was proposed to solve this model. The specific improvements of algorithm included: a three-dimensional vector coding schemne and the corresponding decoding scheme were designed, an effective dynamic neighborhood search operation was introduced to improve the local search ability of the algorithm in the follow bee search stage, and a strategy for generating new food sources was proposed to increase the diversity of the population in the bee detection stage. Finally, the experimental study and algorithm comparison were carried out to verify the validity of the established model and the proposed algorithm.

Key words: green flexible job-shop scheduling, multi-objective optimization, environmental pollution, artificial bee colony(ABC) algorithm

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