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
Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete en...Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete ensemble intrinsic time-scale decomposition （CEITD） and LSSVM optimized by the hybrid differential evolution and particle swarm optimization （HDEPSO） algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components （PRCs） and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.展开更多
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.
基金Project supported by the National High-Tech R&D Program(863)of China(No.2014AA041501)
文摘Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete ensemble intrinsic time-scale decomposition （CEITD） and LSSVM optimized by the hybrid differential evolution and particle swarm optimization （HDEPSO） algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components （PRCs） and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.
