混流汽车液压电子制动控制单元装配线平衡问题设计与研究
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摘要
为了有效解决混流汽车液压电子制动控制单元装配线平衡问题,提出了基于优化遗传算法的求解方法。首先对于复杂的液压电子制动控制单元装配线的工艺装配过程进行设计与计算,分析液压电子制动控制单元的生产工艺步序、工作站数、生产节拍、以最小化生产节拍、最小化工作站数以及均衡各工作站的加权平均负荷进行多目标优化。同时构建液压制动控制单元装配工艺平衡问题的测量模型,并进行案例分析,利用模型求解。最后,以某独资汽车零部件工厂的汽车液压电子制动控制单元装配线为例,对工艺过程进行数学建模,采用优化遗传算法进行求解。结果表明,所提方法与测量模型可为汽车液压电子制动控制单元装配线平衡问题提供可行的解决方案。
In order to solve the mixed model HECU assembly line balancing problem properly,the solution which is based on the optimized genetic algorithm is presented. First of all,regarding the manufacturing process design and calculation of complex HECU assembly line,analyzing the production sequence,amounts of stations,production cycle time,with the goal of minimizing the production cycle time,quantity of workstations and balancing weighted average workload,the idea of multiple objectives optimization is purposed. Meanwhile,setup the mathematic model of HECU assembly balancing problem,analyze the company case,do the model calculation. At last,taking one HECU assembly line of auto components company as the example,the process is modeled,the results are calculated via improved genetic algorithm. The results indicates that the idea and the mathematical method could provide the feasible solution for the HECU balancing problems.
In order to solve the mixed model HECU assembly line balancing problem properly,the solution which is based on the optimized genetic algorithm is presented. First of all,regarding the manufacturing process design and calculation of complex HECU assembly line,analyzing the production sequence,amounts of stations,production cycle time,with the goal of minimizing the production cycle time,quantity of workstations and balancing weighted average workload,the idea of multiple objectives optimization is purposed. Meanwhile,setup the mathematic model of HECU assembly balancing problem,analyze the company case,do the model calculation. At last,taking one HECU assembly line of auto components company as the example,the process is modeled,the results are calculated via improved genetic algorithm. The results indicates that the idea and the mathematical method could provide the feasible solution for the HECU balancing problems.
引文
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[20]Simaria A S,Vilarinho P M. A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II[J]. Computers&Industrial Engineering,2004,47(4):391~407.
[21]Scholl A,Vo BS. Simple assembly line balancing—Heuristic approaches[J]. Journal of Heuristics,1997,2(3):217~244.
[2]苏平,于兆勤.混流装配线平衡问题的多目标优化方法研究[J].中国机械工程,2009(19):2342~2347.
[3]凌文曙.基于遗传算法的混流装配线工作站平衡研究[J].合肥工业大学学报:自然科学版,2008,31(8):1287~1291.
[4]彭慧,徐克林,佀占华.采用遗传算法的混流装配线平衡多目标优化[J].现代制造工程,2011(11):49~53.
[5]肖中华,邓明星,唐秋华.遗传算法在装配线平衡中的应用[J].价值工程,2010,29(2):253~255.
[6]严能,徐子奇,舒帆.基于Lingo的发动机装配线多目标平衡问题研究[J].物流工程与管理,2015(3):1674~4993.
[7]李智,姜兆亮,刘文平.基于NSPSO算法的混合装配线平衡问题多目标优化[J].农业机械学报,2013(10):1000~1298.
[8]潘国强,冯定忠.基于SIMIO仿真的随机型点击装配线优化研究[J].工业工程与管理,2013(2):1007~5429.
[9]吴尔飞,金烨,胡小锋,沈健.基于分支界定方法的双边装配线平衡[J].东华大学学报,2006(4).
[10]陈建国,雷毅华,刘普爱,陈泳钊,温德刚.基于启发式算法的电控板装配线线平衡优化[J].工业工程,2012(2):1007~7375.
[11]查靓,徐学军,余建军,宋莉波.多类约束下U型装配线平衡建模研究[J].工业工程与管理,2011(1):1007~5429.
[12]晁永生,孙文磊,袁亮.平衡装配线的多目标优化[J].计算机集成制造系统,2016(5).
[13]齐相龙,刘晋飞,陈明.汽车线速预装配线平衡问题的优化与仿真[J].机械设计,2015(1):1001~2354.
[14]龙海峰,王熙法.锦标赛选择交互式遗传算法及其应用[J].小型计算机系统,2009(9).
[15]廖美英,张勇军.灾变算子在遗传算法中的作用与研究[J].计算机工程与应用,2005(5).
[16]周永华,赵平,毛宗源.实数编码遗传算法离散重组算子分析[J].华南理工大学学报(自然科学版),2003(01).
[17]王岚.基于自适应交叉和变异概率的遗传算法收敛性研究[J].云南师范大学学报(自然科学版),2010(05).
[18]Bukchin J,Dar-El E M,Rubinovitz J. Mixed model assembly line design in a make-to-order environment[J]. Computers&Industrial Engineering,2002,41(4):405~421.
[19]Su P,Lu Y. Combining Genetic Algorithm and Simulation for the Mixed-Model Assembly Line Balancing Problem[C]//International Conference on Natural Computation. IEEE Computer Society,2007:314~318.
[20]Simaria A S,Vilarinho P M. A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II[J]. Computers&Industrial Engineering,2004,47(4):391~407.
[21]Scholl A,Vo BS. Simple assembly line balancing—Heuristic approaches[J]. Journal of Heuristics,1997,2(3):217~244.