首页|基于熵增强混沌遗传算法的柔性作业车间调度

基于熵增强混沌遗传算法的柔性作业车间调度

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为使企业获得最优综合调度质量的车间调度方案,研究了多目标柔性作业车间调度数学模型及其求解算法,建立了基于最大完工时间、最大机器负荷差、机器总负荷和调度复杂度 4 个调度质量指标的多目标柔性作业车间调度问题模型(MFJSP),提出熵增强混沌遗传算法(ECGA)求解该模型,应用伯努利混沌映射公式改进算法选择操作,用高斯云模型改进变异算子和交叉算子,提高算法的全局寻优能力和搜索效率.根据计算的交叉概率和变异概率执行切牌式交叉操作和两基因片段式变异操作提高种群基因的多样性.以M8J12P3 调度问题为例验证了MFJSP模型和ECGA算法的有效性.结果表明,与SGA、PSO和ABC相比,ECGA具有更快的收敛速度和更好的全局搜索能力,有助于企业提高生产效率和降低成本.
Flexible Job-Shop Scheduling Based on Entropy-Enhanced Chaotic Genetic Algorithm
In order to make enterprises obtain the job-shop scheduling scheme with the optimal comprehen-sive scheduling quality,the multi-objective flexible job-shop scheduling mathematical model and its solution algorithm are studied.A multi-objective flexible job-shop scheduling problem(MFJSP)model is estab-lished based on four optimization objectives:maximum completion time,maximum machine load differ-ence,total machine load and scheduling complexity.Entropy-enhanced chaotic genetic algorithm(ECGA)is proposed to solve the model.The Bernoulli chaotic mapping formula is used to improve the algorithm se-lection operation,and the Gaussian cloud model is used to improve the crossover operator and mutation op-erator to improve the global optimization ability and search efficiency of the algorithm.According to the calculated crossover probability and mutation probability,the playing-cards-cutting crossover operation and two-gene-segment mutation operation are performed to improve the genetic diversity of the population.Tak-ing M8J12P3 scheduling problem as an example,the effectiveness of MFJSP model and ECGA algorithm is verified.The result shows that ECGA has faster convergence speed and better global search ability than SGA,PSO and ABC,which helps enterprises improve production efficiency and reduce costs.

flexible job-shop schedulinggenetic algorithmmulti-objective optimizationscheduling quality

李永湘、姚锡凡

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贵州工程应用技术学院机械工程学院,毕节 551700

华南理工大学机械与汽车工程学院,广州 510640

柔性作业车间调度 遗传算法 多目标优化 调度质量

国家自然科学基金毕节市科学技术项目毕节市科学技术项目贵州省高等学校自然科学研究项目贵州工程应用技术学院科研项目贵州工程应用技术学院科研项目

51375168毕科联合字G[2019]8毕科联合[2023]9黔教技[2023]047号院科合字G2018009ZY202101

2024

10.13462/j.cnki.mmtamt.2024.03.040

组合机床与自动化加工技术
大连组合机床研究所 中国机械工程学会生产工程分会

组合机床与自动化加工技术

CSTPCD北大核心
影响因子：0.671
ISSN：1001-2265
年,卷(期)：2024.(3)
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