随机型装配线平衡与缓冲区配置集成优化
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摘要
线平衡问题和缓冲区配置问题在装配线设计中常被分开独立研究,二者之间存在复杂的交互影响关系.随机型装配线由于操作时间波动加剧了二者之间的相互作用与影响,先平衡再缓冲区配置的串行优化模式难以得到全局最优结果,因此需要在线平衡的同时协同解决缓冲区配置问题.针对随机型装配线规划问题,在基于操作时间状态熵的工位复杂度定义基础上,以生产率最大,整线工位复杂度均衡指数、标准操作时间均衡指数和缓冲区总量最小为优化目标,构建线平衡和缓冲区配置集成优化模型.采用参数化建模仿真获取整线生产率,通过改进的遗传算法获取集成优化方案.最后对变速箱装配线实例进行计算验证,证明了该方法的有效性.
Line balancing problem and buffer allocation problem are often studied separately,but there is a complex interaction between them. Considering stochastic assembly line,the operating time variability exacerbates the interaction between the two problems.Optimizing sequentially is difficult to get the global optimal solution.Therefore,it is necessary to solve the two problems simultaneously. The probability distribution of operating time is measured as station complexity based on information entropy.An integrated optimization model is established. The optimization objectives are as follows:maximizing of the production rate and minimizing of the smoothness index of standard workstation time,the smoothness index of workstation complexity and total buffer capacities.Parametric modeling simulation is used to calculate production rate.An improved genetic algorithm is put forward to obtain integrated optimization solution.An instance of a gearbox assembly line is calculated and verified,which proves the effectiveness of the method.
Line balancing problem and buffer allocation problem are often studied separately,but there is a complex interaction between them. Considering stochastic assembly line,the operating time variability exacerbates the interaction between the two problems.Optimizing sequentially is difficult to get the global optimal solution.Therefore,it is necessary to solve the two problems simultaneously. The probability distribution of operating time is measured as station complexity based on information entropy.An integrated optimization model is established. The optimization objectives are as follows:maximizing of the production rate and minimizing of the smoothness index of standard workstation time,the smoothness index of workstation complexity and total buffer capacities.Parametric modeling simulation is used to calculate production rate.An improved genetic algorithm is put forward to obtain integrated optimization solution.An instance of a gearbox assembly line is calculated and verified,which proves the effectiveness of the method.
引文
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[12]HARRIS H J,POWELL G S.An algorithm for optimal buffer placement in reliable serial lines[J].IIE Transactions,1999,31(4):287.
[13]NILS B,FLIEDNER M,SCHOLL A.Assembly line balancing:which model to use when?[J].International Journal of Production Economics,2008,111(2):509.
[14]潘国强,冯定忠.基于SIMIO仿真的随机型电机装配线优化研究[J].工业工程与管理,2013,18(2):65.PAN Guoqiang,FENG Dingzhong.A simulated SIMIO-based optimization approach to a stochastic motor assembly line[J].Engineering and Management,2013,18(2):65.
[15]LORENZO T.Coupling agenetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times[J].International Journal of Production Economics,2015,159(C):319.
[16]张良安,梅江平,黄田.基于遗传算法的机器人自动化生产线缓冲区容量分配方法[J].机械工程学报,2010,46(11):101.ZHANG Liangan,MEI Jiangping,HUANG Tian.Genetic algorithm-based buffer allocation of a robot production line[J].Journal of Mechanical Engineering,2010,46(11):101
[2]NKASU M M,LEUNG K H.A stochastic approach to assembly line balancing[J].International Journal of Production Research,1995,33(4):975.
[3]宋林,张则强,程文明,等.随机型双边装配线平衡问题的一种启发式算法[J].工业工程,2011,14(4):129.SONG Lin,ZHANG Zeqiang,CHENG Wenming,Heuristic for two-sided stochastic assembly line balancing[J].Industrial Engineering Journal,2011,14(4):129.
[4]ADIL Baykasoˇglu,LALEzbakir.Stochastic U-line balancing using genetic algorithms[J].International Journal of Advanced Manufacturing Technology,2007,32(1-2):139.
[5]周亮,宋华明,韩玉启.基于遗传算法的随机型生产线负荷平衡[J].机械制造,2003,41(3):23.ZHOU Liang,SONG Huaming,HAN Yuqi.Genetic algorithm-based stochastic production line balancing[J].Machinery,2003,41(3):23.
[6]刘俨后,左敦稳,张丹,等.随机作业时间的装配线平衡问题[J].计算机集成制造系统,2014,20(6):1372.LIU Yanhou,ZUO Dunwen,ZHANG Dan,et al.Assembly line balancing with stochastic operation times[J].Computer Integrated Manufacturing Systems,2014,20(6):1372.
[7]FRIZELLE G,WOODALL P.Big data,data loss and observation analysis[C]∥International Conference on Information Quality.Ciudad Real:Escuela Superior de Informática Ciudad Real,2016:1-6.
[8]KONSTANTINOS Efthymiou.Manufacturing systems complexity:an assessment of manufacturing performance indicators unpredictability[J].CIRP Journal of Manufacturing Science&Technology,2014,7(4):324.
[9]WANG H,HU S J.Manufacturing complexity in assembly systems with hybrid configurations and its impact on throughput[J].CIRP Annals-Manufacturing Technology,2010,59(1):53.
[10]何非,饶运清,邵新宇.基于装配关系复杂性的装配线动态平衡问题[J].计算机集成制造系统,2013,19(1):29.HE Fei,RAO Yunqing,SHAO Xinyu.Assembly line dynamic balancing problem based on assembly relationship complexity[J].Computer Integrated Manufacturing Systems,2013,19(1):29.
[11]HILLLER F S,SO K.C.Some data for applying the bowl phenomenon to large production line systems[J].International Journal of Production Research,1993,31(4):811.
[12]HARRIS H J,POWELL G S.An algorithm for optimal buffer placement in reliable serial lines[J].IIE Transactions,1999,31(4):287.
[13]NILS B,FLIEDNER M,SCHOLL A.Assembly line balancing:which model to use when?[J].International Journal of Production Economics,2008,111(2):509.
[14]潘国强,冯定忠.基于SIMIO仿真的随机型电机装配线优化研究[J].工业工程与管理,2013,18(2):65.PAN Guoqiang,FENG Dingzhong.A simulated SIMIO-based optimization approach to a stochastic motor assembly line[J].Engineering and Management,2013,18(2):65.
[15]LORENZO T.Coupling agenetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times[J].International Journal of Production Economics,2015,159(C):319.
[16]张良安,梅江平,黄田.基于遗传算法的机器人自动化生产线缓冲区容量分配方法[J].机械工程学报,2010,46(11):101.ZHANG Liangan,MEI Jiangping,HUANG Tian.Genetic algorithm-based buffer allocation of a robot production line[J].Journal of Mechanical Engineering,2010,46(11):101