以往车间布局和调度优化都是各自分开进行的,单独车间布局优化时一般假设待加工工件各工序的加工设备已经确定;而单独调度优化则在车间布局确定后进行的,这种分开优化的方式忽略了不同布局对工序加工设备间距离的影响,由此影响工序间的...以往车间布局和调度优化都是各自分开进行的,单独车间布局优化时一般假设待加工工件各工序的加工设备已经确定;而单独调度优化则在车间布局确定后进行的,这种分开优化的方式忽略了不同布局对工序加工设备间距离的影响,由此影响工序间的搬运时间,从而影响调度结果。为此,提出以车间制造过程总碳排放和总完工时间最小为优化目标的车间布局和调度集成优化模型。为了求解该模型,设计多目标果蝇优化算法(Multi-objective fruit fly optimization algorithm,MFOA)。为了增强MFOA全局搜索能力和维持算法的稳定性,设计一种基于混合步长的嗅觉搜索;为了增大果蝇种群的协作,避免陷入局部最优引入了全局协作机制。将非支配等级排序方法引入MFOA处理多目标问题,并用算例验证了多目标果蝇优化算法的有效性。将集成优化结果与车间机群式布局下的调度优化结果和将车间布局、调度分开优化的结果分别进行对比,说明提出的集成优化模型可以得到更低的碳排放,验证了模型的有效性。展开更多
In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objectiv...In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.展开更多
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 m...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.展开更多
文摘以往车间布局和调度优化都是各自分开进行的,单独车间布局优化时一般假设待加工工件各工序的加工设备已经确定;而单独调度优化则在车间布局确定后进行的,这种分开优化的方式忽略了不同布局对工序加工设备间距离的影响,由此影响工序间的搬运时间,从而影响调度结果。为此,提出以车间制造过程总碳排放和总完工时间最小为优化目标的车间布局和调度集成优化模型。为了求解该模型,设计多目标果蝇优化算法(Multi-objective fruit fly optimization algorithm,MFOA)。为了增强MFOA全局搜索能力和维持算法的稳定性,设计一种基于混合步长的嗅觉搜索;为了增大果蝇种群的协作,避免陷入局部最优引入了全局协作机制。将非支配等级排序方法引入MFOA处理多目标问题,并用算例验证了多目标果蝇优化算法的有效性。将集成优化结果与车间机群式布局下的调度优化结果和将车间布局、调度分开优化的结果分别进行对比,说明提出的集成优化模型可以得到更低的碳排放,验证了模型的有效性。
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.70671057)教育部博士点基金(No.20051065002)青岛市自然科学基金(the Natural Science Foundation of Qingdao City of China under Grant No.03-2-jz-19)
文摘In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.
基金Innovation Method Fund of China(No.2019IM020200)Joint Funds of the National Natural Science Foundation of China(No.U1904210-4)+2 种基金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)。
文摘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.
