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作 者:Wang Yansen Feng Lijie Wang Jinfeng Liu Peng Zhao Huadong 王彦森;冯立杰;王金凤;刘鹏;赵华东(郑州大学机械与动力工程学院,郑州450001;上海海事大学中国(上海)自贸区供应链研究院,上海201306;郑州大学管理学院,郑州450001;河南省创新方法工程技术研究中心,郑州450001)
机构地区:[1]School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou 450001,China [2]China Institute of FTZ Supply Chain,Shanghai Maritime University,Shanghai 201306,China [3]School of Management,Zhengzhou University,Zhengzhou 450001,China [4]Henan Engineering Research Center of Innovation Method,Zhengzhou 450001,China
出 处:《Journal of Southeast University(English Edition)》2022年第4期350-362,共13页东南大学学报(英文版)
基 金:Innovation Method Fund of China(No.2019IM020200);Joint Funds of the National Natural Science Foundation of China(No.U1904210-4);Zhengzhou University Support Program Project for Young Talents and Enterprise Cooperative Innovation Team;“Intelligent Manufacturing Comprehensive Standardization and New Model Application Project”of Ministry of Industry and Information Technology(No.2017ZNZX02);Shanghai Science and Technology Program(No.20040501300)。
摘 要:Aiming at the machining process of high-performance bearing parts,the green shop scheduling problem of bearing parts processing was studied herein,with the maximum completion time,minimum machine carbon emission,and minimum grinding fluid usage as the optimization objectives.The manufacturing process is divided into six technological processes:startup,clamping,machining,unloading,standby,and shutdown.The multiobjective green shop scheduling mathematical model is established.Then,an improved multiobjective genetic algorithm is proposed,adopting a segmented coding method that integrates the process and machine selections and improves the steps of crossover and mutation,all of which improve the algorithm s convergence.Finally,the bearing parts processing of a bearing company is taken as a case study,and large-scale data tests and analyses are constructed.The result shows that the proposed model can obtain lower completion time,carbon emission,and grinding fluid consumption,which verifies the scientificity and effectiveness of the proposed model.针对高性能轴承零部件的加工过程,以最大完工时间、机器加工碳排放量和磨削液使用量为目标函数,对轴承零部件加工的绿色车间调度问题进行研究.将轴承零部件的加工过程细化为启动、装夹、加工、卸夹、待机和关闭共6个流程,建立多目标绿色调度数学模型;提出了改进的多目标遗传算法进行求解,采用融合工序选择和机器选择的分段式编码方式,并在交叉、变异等步骤进行改进,提高算法收敛性;最后以某轴承公司的轴承零部件加工过程为例进行案例分析与对比试验,并通过大规模数据测试与分析.结果表明,所提模型可以得到更低的完工时间、碳排放量和磨削液使用量,从而验证了所提模型的科学性和有效性.
关 键 词:green shop scheduling problem(GSSP) multiobjective optimization carbon emissions rolling bearing
分 类 号:TH278[机械工程—机械制造及自动化] TP30[自动化与计算机技术—计算机系统结构]
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