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.
