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作 者:王科[1] 李西兴 WANG Ke;LI Xixing(School of Mechanical Engineering,Hubei Univ.of Tech.,Wuhan 430068,China)
机构地区:[1]湖北工业大学机械工程学院,湖北武汉430068
出 处:《湖北工业大学学报》2025年第1期20-24,42,共6页Journal of Hubei University of Technology
摘 要:针对离散型制造企业的第Ⅱ类装配线平衡问题,构建以最小生产节拍、最小负载标准差为优化目标的数学模型.在经典遗传算法的基础上,设计一种基于精英保留策略的种群分离机制以维持种群的基因多样性,并根据算法的迭代进程引入自适应交叉与自适应变概率操作;为提高算法的局部搜索能力,嵌入模拟退火算法的MeG tropolis准则,形成一种混合遗传模拟退火算法;通过标准案例集的12组实验,相较于遗传算法,混合遗传模拟退火算法在求解能力上具有明显优势.进一步,以某企业的青贮机装配线为案例,应用混合遗传模拟退火算法进行求解,结果显示该模型的有效性和混合遗传模拟退火算法的优越性.生产实例验证结果表明,优化后的装配线生产周期缩短为50.4s,线平衡率提升至93.52%,显著提升了农机企业装配线的效率.For the Type-Ⅱof assembly line balancing problem in discrete manufacturing enterprises,a mathematical model with minimum production beat and minimum load standard deviation as the optimization objectives has been established.Based on the classical genetic algorithm,a population separation mechanism based on elite retention strategy has been designed to retain the genetic diversity of the population,and adaptive crossover and adaptive variable probability has been introduced according to the number of iterations of the algorithm;the Metropolis criterion of simulated annealing has been embedded to prevent the algorithm from falling into local optimum,which makes the hybrid genetic simulated annealing algorithm designed in this paper have stronger global search capability.Finally,taking the silage machine assembly line of an enterprise as an example,the hybrid genetic simulated annealing algorithm has been used to solve the problem.Compared with the genetic algorithm,the improved genetic algorithm and the improved particle swarm optimization algorithm.The validity of the model and the superiority of the hybrid genetic simulated annealing algorithm have been verified by the comparison results.The production example shows that the optimized assembly line production beat was 50.4s,and the line balance rate reaches 93.52%,which effectively improves the production efficiency of the assembly line of agricultural machinery enterprises.
关 键 词:装配线平衡 生产节拍 负载标准差 混合遗传模拟退火算法
分 类 号:TH181[机械工程—机械制造及自动化]
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