基于机床加工匹配特性的混合流水车间绿色生产调度
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摘要
针对混合流水车间绿色生产过程中的设备选择和调度目标匹配问题,提出基于机床加工特性的多目标调度模型和改进遗传算法。该算法建立了混合流水车间调度的时间、能耗与成本优化模型,采用模糊隶属方法描述了机床加工特性,在遗传算法求解过程中通过机床加工特性隶属度与调度目标的权重系数匹配关系,建立了自适应的交叉、变异和优势保留策略,在每一代迭代中提高在调度目标方向上的选择压力,加速收敛。通过实例分析对比了不同算法的优化结果,从而验证了模型及算法的有效性,并提出了高效、节能、经济和综合4种调度生产模式,为混合流水车间绿色生产提供了指导。
To solve the problem of hybrid flow-shop sustainable scheduling, a multi-objective optimization model and modified genetic algorithm were proposed based on the performance of machine tools. The production time, energy consumption and cost in hybrid flow-shop were elaborated in the proposed mathematical model, and the performance of the machine tools in hybrid-shop were described with fuzzy membership method. In the modified GA algorithm, the adaptive crossover, mutation and elitist strategy were established according to the relationship between the membership degree of machine tools and weights of optimization objectives. Thus the selection pressure to the proper direction could be obtained so as to accelerate the convergence speed in the iteration of GA. A case study was used to verify the established model and algorithm by comparing the results obtained by different GA methods. Based on the optimization results, four kinds of typical workshop production scheduling modes including high efficiency, energy conservation, economy and comprehensive production were put forward to provide guidance for the sustainable production of hybrid flow-shop scheduling.
To solve the problem of hybrid flow-shop sustainable scheduling, a multi-objective optimization model and modified genetic algorithm were proposed based on the performance of machine tools. The production time, energy consumption and cost in hybrid flow-shop were elaborated in the proposed mathematical model, and the performance of the machine tools in hybrid-shop were described with fuzzy membership method. In the modified GA algorithm, the adaptive crossover, mutation and elitist strategy were established according to the relationship between the membership degree of machine tools and weights of optimization objectives. Thus the selection pressure to the proper direction could be obtained so as to accelerate the convergence speed in the iteration of GA. A case study was used to verify the established model and algorithm by comparing the results obtained by different GA methods. Based on the optimization results, four kinds of typical workshop production scheduling modes including high efficiency, energy conservation, economy and comprehensive production were put forward to provide guidance for the sustainable production of hybrid flow-shop scheduling.
引文
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[16] COSTA A,CAPPADONNA F A,FICHERA S.A novel genetic algorithm for the hybrid flow shop scheduling with parallel batching and eligibility constraints[J].International Journal of Advanced Manufacturing Technology,2014,75(5/6/7/8):833-847.
[17] YIN Lyujiang,LI Xinyu,GAO Liang,et al.Energy-efficient job shop schedluing problem with variable spindle speed using a novel multi-objective algorithm[J].Advances in Mechanical Engineering.2017,9(4):1-21.
[2] WANG Shengyan,WANG Ling,LIU Min,et al.Estimation of distribution algorithm for solving hybrid flow-shop scheduling problem with identical parallel machine[J].International Journal of Advanced Manufacturing Technology,2013,68(9/10/11/12):2043-2056.
[3] ATTAR S F,MOHAMMADI M,TAVAKKOLI-MOGHAD-DAM R.Hybrid flexible flowshop scheduling problem with unrelated parallel machines and limited waiting times[J].International Journal of Advanced Manufacturing Technology,2013,68(5/6/7/8):1583-1599.
[4] LI Yuxia,CAO Huajun,LI Hongcheng,et al.Carbon emissions dynamic characteristic and two stage optimization scheduling model for job shop[J].Computer Integrated Manufacturing Systems,2015,21(10):2687-2693(in Chinese).[李玉霞,曹华军,李洪丞,等.作业车间碳排放动态特性及二阶优化调度模型[J].计算机集成制造系统,2015,21(10):2687-2693.]
[5] LI Congbo,CUI Longguo,LIU Fei,et al.Multi-objective NC machining parameters optimization model for high efficiency and low carbon[J].Journal of Mechanical Engineering,2013,49(9):87-96(in Chinese).[李聪波,崔龙国,刘飞,等.面向高效低碳的数控加工参数多目标优化模型[J].机械工程学报,2013,49(9):87-96.]
[6] HAN Zhonghua,DONG Xiaoting,SHI Haibo,et al.Improved DE algorithm for hybrid flow shop load balancing scheduling problem[J].Computer Integrated Manufacturing Systems,2016,22(2):547-557(in Chinese).[韩忠华,董晓婷,史海波,等.改进DE算法求解混合流水车间负荷平衡问题[J].计算机集成制造系统,2016,22(2):547-557.]
[7] ZHAN Yong,QIU Changhua,XUE Kai.A hybrid genetic agorithm for hybrid flow shop scheduling with load balancing[J].Key Engineering Materials,2009,392/393/394:250-255.
[8] LUO Hao,DU Bing,HUANG G Q,et al.Hybrid flow shop scheduling considering machine electricity consumption cost[J].International Journal of Production Economics,2013,146(2):423-439.
[9] WEI Zhong,XU Xiaofei,DENG Shengchun.Evolutionary algorithm for solving multi-objective hybrid flow-shop scheduling problem[J].Computer Integrated Manufacturing Systems,2006,12(8):1227-1234(in Chinese).[卫忠,徐晓飞,邓胜春.多目标混合流水车间作业调度的演化算法[J].计算机集成制造系统,2006,12(8):1227-1234.]
[10] CUI Qi,WU Xiuli,YU Jianjun.Improved genetic algorithm variable neighborhood search for solving hybrid flow shop scheduling problem[J].Computer Integrated Manufacturing Systems,2017,23(9):1917-1927(in Chinese).[崔琪,吴秀丽,余建军.变邻域改进遗传算法求解混合流水车间调度问题[J].计算机集成制造系统,2017,23(9):1917-1927.]
[11] LAHIMER A,LOPEZ P,HAOURRI M.Improved bounds for hybrid flow shop scheduling with multiprocessor tasks[J].Computers & Industrial Engineering,2013,66(4):1106-1114.
[12] WANG Wei.Research on shop scheduling optimization methods for energy and its application development environment[D].Harbin:Harbin Institute of Technology,2011(in Chinese).[王伟.面向能耗优化的车间调度方法研究及其应用环境开发[D].哈尔滨:哈尔滨工业大学,2011.]
[13] KONG Lulu.Research on CNC machine tool energy modeling and analysis of its influential parameters[D].Dalian:Dalian University of Technology,2014(in Chinese).[孔露露.数控机床能耗建模与参数影响分析[D].大连:大连理工大学,2014.]
[14] VELCHEV S,KOLEV I,IVANOV K,et al.Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning[J].Journal of Cleaner Production,2014,80:139-149.
[15] DONG Wenyong.Techniques and mathematical modeling for optimization[M].Beijing:Tsinghua University Press,2010(in Chinese).[董文永.最优化技术与数学建模[M].北京:清华大学出版社,2010.]
[16] COSTA A,CAPPADONNA F A,FICHERA S.A novel genetic algorithm for the hybrid flow shop scheduling with parallel batching and eligibility constraints[J].International Journal of Advanced Manufacturing Technology,2014,75(5/6/7/8):833-847.
[17] YIN Lyujiang,LI Xinyu,GAO Liang,et al.Energy-efficient job shop schedluing problem with variable spindle speed using a novel multi-objective algorithm[J].Advances in Mechanical Engineering.2017,9(4):1-21.