It is urgent to effectively improve the production efficiency in the running process of manufacturing systems through a new generation of information technology.According to the current growing trend of the internet o...It is urgent to effectively improve the production efficiency in the running process of manufacturing systems through a new generation of information technology.According to the current growing trend of the internet of things(IOT)in the manufacturing industry,aiming at the capacitor manufacturing plant,a multi-level architecture oriented to IOT-based manufacturing environment is established for a flexible flow-shop scheduling system.Next,according to multi-source manufacturing information driven in the manufacturing execution process,a scheduling optimization model based on the lot-streaming strategy is proposed under the framework.An improved distribution estimation algorithm is developed to obtain the optimal solution of the problem by balancing local search and global search.Finally,experiments are carried out and the results verify the feasibility and effectiveness of the proposed approach.展开更多
An important production planning problem is how to best schedule jobs(or lots)when each job consists of a large number of identical parts.This problem is often approached by breaking each job/lot into sublots(termed l...An important production planning problem is how to best schedule jobs(or lots)when each job consists of a large number of identical parts.This problem is often approached by breaking each job/lot into sublots(termed lot streaming).When the total number of transfer sublots in lot streaming is large,the computational effort to calculate job completion time can be significant.However,researchers have largely neglected this computation time issue.To provide a practical method for production scheduling for this situation,we propose a method to address the n-job,m-machine,and lot streaming flow-shop scheduling problem.We consider the variable sublot sizes,setup time,and the possibility that transfer sublot sizes may be bounded because of capacity constrained transportation activities.The proposed method has three stages:initial lot splitting,job sequencing optimization with efficient calculation of the makespan/total flow time criterion,and transfer adjustment.Computational experiments are conducted to confirm the effectiveness of the three-stage method.The experiments reveal that relative to results reported on lot streaming problems for five standard datasets,the proposed method saves substantial computation time and provides better solutions,especially for large-size problems.展开更多
基金supported by the National Natural Science Foundations of China(No. 51875171)
文摘It is urgent to effectively improve the production efficiency in the running process of manufacturing systems through a new generation of information technology.According to the current growing trend of the internet of things(IOT)in the manufacturing industry,aiming at the capacitor manufacturing plant,a multi-level architecture oriented to IOT-based manufacturing environment is established for a flexible flow-shop scheduling system.Next,according to multi-source manufacturing information driven in the manufacturing execution process,a scheduling optimization model based on the lot-streaming strategy is proposed under the framework.An improved distribution estimation algorithm is developed to obtain the optimal solution of the problem by balancing local search and global search.Finally,experiments are carried out and the results verify the feasibility and effectiveness of the proposed approach.
基金Project supported by the National Natural Science Foundation of China(No.61403163)the Zhejiang Provincial Natural Science Foundation of China(Nos.LQ14G010008 and LY15F030021)
文摘An important production planning problem is how to best schedule jobs(or lots)when each job consists of a large number of identical parts.This problem is often approached by breaking each job/lot into sublots(termed lot streaming).When the total number of transfer sublots in lot streaming is large,the computational effort to calculate job completion time can be significant.However,researchers have largely neglected this computation time issue.To provide a practical method for production scheduling for this situation,we propose a method to address the n-job,m-machine,and lot streaming flow-shop scheduling problem.We consider the variable sublot sizes,setup time,and the possibility that transfer sublot sizes may be bounded because of capacity constrained transportation activities.The proposed method has three stages:initial lot splitting,job sequencing optimization with efficient calculation of the makespan/total flow time criterion,and transfer adjustment.Computational experiments are conducted to confirm the effectiveness of the three-stage method.The experiments reveal that relative to results reported on lot streaming problems for five standard datasets,the proposed method saves substantial computation time and provides better solutions,especially for large-size problems.
