基于遗传算法的卷烟换牌排产与优化设计
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摘要
为解决卷烟生产换牌耗时长等问题,采用遗传算法对卷烟换牌排产进行了优化设计。结合卷烟换牌生产情况,分析了换牌耗时与设备规格调整之间的关联特性;通过增大变异概率和交叉概率对遗传算法进行改进,防止算法陷入局部最优解;改进后遗传算法能够快速找到全局最优解,计算得到最短换牌耗时并给出最优排产方案。仿真结果表明,寻找到的最优排产方案可节约时间240 min;根据仿真最优排序进行卷烟排产试验,结果显示优化后换牌耗时比优化前缩短105 min,有效提高了生产效率。该方法可为完善卷烟生产计划、优化调度策略提供技术支持。
In order to rationalize cigarette brand change in production, cigarette production scheduling was optimized with genetic algorithm. On the basis of production conditions, the relations between the duration of brand change and machine adjustment was analyzed; and genetic algorithm was modified by increasing probability of mutation and probability of crossover to prevent the algorithm from falling into the local optimization. The modified genetic algorithm could quickly find out the global optimal solution, give out the shortest time needed for brand change and the optimal production schedule. The simulation results showed that the optimal production schedule could save time by 240 min. An experiment carried out according to the optimal simulation schedule indicated that the time consumed for brand change was shortened by 105 min, and the production efficiency was promoted effectively. This method provides technical supports for perfecting cigarette production plan and optimizing scheduling strategy.
In order to rationalize cigarette brand change in production, cigarette production scheduling was optimized with genetic algorithm. On the basis of production conditions, the relations between the duration of brand change and machine adjustment was analyzed; and genetic algorithm was modified by increasing probability of mutation and probability of crossover to prevent the algorithm from falling into the local optimization. The modified genetic algorithm could quickly find out the global optimal solution, give out the shortest time needed for brand change and the optimal production schedule. The simulation results showed that the optimal production schedule could save time by 240 min. An experiment carried out according to the optimal simulation schedule indicated that the time consumed for brand change was shortened by 105 min, and the production efficiency was promoted effectively. This method provides technical supports for perfecting cigarette production plan and optimizing scheduling strategy.
引文
[1]谢剑平.形势与未来:烟草科技发展展望[J].中国烟草学报,2017,23(3):1-7.XIE Jianping.On the development prospects of tobacco science and technology in China[J].Acta Tabacaria Sinica,2017,23(3):1-7.
[2]王小军,朱道飞,王建军.卷烟企业能效指标体系的构建与应用[J].节能,2014(7):7-10.WANG Xiaojun,ZHU Daofei,WANG Jianjun.Construction and application of the energy efficiency index system in cigarette manufactures[J].Energy Conservation,2014(7):7-10.
[3]谢瑞波.卷烟企业优化排产及调度系统分析与设计[D].昆明:云南大学,2013.XIE Ruibo.Analysis and design of optimized cigarette brand change scheduling system for cigarette enterprises[D].Kunming:Yunnan University,2013.
[4]邓灿勇,陈志刚,赵明.遗传算法在烟草企业排产中的应用研究[J].电脑与信息技术,2010,18(6):13-15.DENG Canyong,CHEN Zhigang,ZHAO Ming.Research on the application of genetic algorithm based advance job shop scheduling in tobacco industry[J].Computer and Information Technology,2010,18(6):13-15.
[5]金剑,金钊,祁跃东.卷烟生产计划排产模型建立与优化[J].计算机工程与应用,2013,49(18):253-259.JIN Jian,JIN Zhao,QI Yuedong.Cigarette production scheduling modeling and optimization[J].Computer Engineering and Applications,2013,49(18):253-259.
[6]陈园园,夏筱筠,柏松,等.粒子群遗传算法在离散制造业排产中的研究[J].计算机系统应用,2016,25(5):94-100.CHEN Yuanyuan,XIA Xiaojun,BAI Song,et al.Research on particle swarm genetic algorithm for scheduling of discrete manufacturing industry[J].Computer Systems&Applications,2016,25(5):94-100.
[7]韩忠华,朱一行,史海波,等.基于改进紧致遗传算法的柔性流水车间组批排产优化问题研究[J].系统工程理论与实践,2016,36(6):1616-1624.HAN Zhonghua,ZHU Yihang,SHI Haibo,et al.Study for the flexible flow shop scheduling problem with batch process machines based on an advanced compact genetic algorithm[J].Systems Engineering-Theory&Practice,2016,36(6):1616-1624.
[8]陈园园.基于PSO_GA混合算法的离散制造业排产方法研究与实现[D].沈阳:中国科学院研究生院(沈阳计算技术研究所),2016.CHEN Yuanyuan.Discrete manufacturing production scheduling method base on PSO_GA hybrid algorithm research and implementation[D].Shenyang:Graduate University of Chinese Academy of Sciences(Shenyang Institute of Computing Technology),2016.
[9]魏巍,谭建荣,冯毅雄,等.基于改进的非支配排序遗传算法的模块化产品多目标配置优化研究[J].计算机集成制造系统,2007,13(11):2092-2098,2161.WEI Wei,TAN Jianrong,FENG Yixiong,et al.Multi-objective configuration optimization of modularized product based on NSGA-Ⅱ[J].Computer Integrated Manufacturing Systems,2007,13(11):2092-2098,2161.
[10]李存兵,谢林君,杨金欣.面向低碳的双层遗传算法烟草物流路径优化[J].烟草科技,2018,51(1):85-92.LI Cunbing,XIE Linjun,YANG Jinxin.Low carbonoriented route optimization of tobacco logistics via double-layer genetic algorithm[J].Tobacco Science&Technology,2018,51(1):85-92.
[11]WANG Binggang.Sequencing mixed-model production systems by modified multi-objective genetic algorithms[J].Chinese Journal of Mechanical Engineering,2011,23(5):537-546.
[12]王军,刘鹏翔,刘羚迪,等.基于卷烟生产计划优化的HPP框架及总生产计划模型[J].烟草科技,2017,50(8):91-96.WANG Jun,LIU Pengxiang,LIU Lingdi,et al.HPPframework and aggregate planning model based on cigarette production plan optimization[J].Tobacco Science&Technology,2017,50(8):91-96.
[13]Hu Y F,Dargush G F,Shao X Y.A conceptual evolutionary aseismic decision support framework for hospitals[J].Earthquake Engineering and Engineering Vibration,2012,11(4):499-512.
[2]王小军,朱道飞,王建军.卷烟企业能效指标体系的构建与应用[J].节能,2014(7):7-10.WANG Xiaojun,ZHU Daofei,WANG Jianjun.Construction and application of the energy efficiency index system in cigarette manufactures[J].Energy Conservation,2014(7):7-10.
[3]谢瑞波.卷烟企业优化排产及调度系统分析与设计[D].昆明:云南大学,2013.XIE Ruibo.Analysis and design of optimized cigarette brand change scheduling system for cigarette enterprises[D].Kunming:Yunnan University,2013.
[4]邓灿勇,陈志刚,赵明.遗传算法在烟草企业排产中的应用研究[J].电脑与信息技术,2010,18(6):13-15.DENG Canyong,CHEN Zhigang,ZHAO Ming.Research on the application of genetic algorithm based advance job shop scheduling in tobacco industry[J].Computer and Information Technology,2010,18(6):13-15.
[5]金剑,金钊,祁跃东.卷烟生产计划排产模型建立与优化[J].计算机工程与应用,2013,49(18):253-259.JIN Jian,JIN Zhao,QI Yuedong.Cigarette production scheduling modeling and optimization[J].Computer Engineering and Applications,2013,49(18):253-259.
[6]陈园园,夏筱筠,柏松,等.粒子群遗传算法在离散制造业排产中的研究[J].计算机系统应用,2016,25(5):94-100.CHEN Yuanyuan,XIA Xiaojun,BAI Song,et al.Research on particle swarm genetic algorithm for scheduling of discrete manufacturing industry[J].Computer Systems&Applications,2016,25(5):94-100.
[7]韩忠华,朱一行,史海波,等.基于改进紧致遗传算法的柔性流水车间组批排产优化问题研究[J].系统工程理论与实践,2016,36(6):1616-1624.HAN Zhonghua,ZHU Yihang,SHI Haibo,et al.Study for the flexible flow shop scheduling problem with batch process machines based on an advanced compact genetic algorithm[J].Systems Engineering-Theory&Practice,2016,36(6):1616-1624.
[8]陈园园.基于PSO_GA混合算法的离散制造业排产方法研究与实现[D].沈阳:中国科学院研究生院(沈阳计算技术研究所),2016.CHEN Yuanyuan.Discrete manufacturing production scheduling method base on PSO_GA hybrid algorithm research and implementation[D].Shenyang:Graduate University of Chinese Academy of Sciences(Shenyang Institute of Computing Technology),2016.
[9]魏巍,谭建荣,冯毅雄,等.基于改进的非支配排序遗传算法的模块化产品多目标配置优化研究[J].计算机集成制造系统,2007,13(11):2092-2098,2161.WEI Wei,TAN Jianrong,FENG Yixiong,et al.Multi-objective configuration optimization of modularized product based on NSGA-Ⅱ[J].Computer Integrated Manufacturing Systems,2007,13(11):2092-2098,2161.
[10]李存兵,谢林君,杨金欣.面向低碳的双层遗传算法烟草物流路径优化[J].烟草科技,2018,51(1):85-92.LI Cunbing,XIE Linjun,YANG Jinxin.Low carbonoriented route optimization of tobacco logistics via double-layer genetic algorithm[J].Tobacco Science&Technology,2018,51(1):85-92.
[11]WANG Binggang.Sequencing mixed-model production systems by modified multi-objective genetic algorithms[J].Chinese Journal of Mechanical Engineering,2011,23(5):537-546.
[12]王军,刘鹏翔,刘羚迪,等.基于卷烟生产计划优化的HPP框架及总生产计划模型[J].烟草科技,2017,50(8):91-96.WANG Jun,LIU Pengxiang,LIU Lingdi,et al.HPPframework and aggregate planning model based on cigarette production plan optimization[J].Tobacco Science&Technology,2017,50(8):91-96.
[13]Hu Y F,Dargush G F,Shao X Y.A conceptual evolutionary aseismic decision support framework for hospitals[J].Earthquake Engineering and Engineering Vibration,2012,11(4):499-512.