Agile Supply Chain Management (ASCM) is an important topic and has received much attention recently. ASCM is a new management technology. Agile Supply Chain Management Decision Support System (ASCM DSS) is presented. ...Agile Supply Chain Management (ASCM) is an important topic and has received much attention recently. ASCM is a new management technology. Agile Supply Chain Management Decision Support System (ASCM DSS) is presented. Firstly, agile supply chain management technology is introduced. Secondly a decision support system for agile supply chain management is proposed. Then, the implementation of ASCM DSS in enterprise is discussed. Finally, a fuzzy intelligence decision making process in Shanghai Turbine Generator Company (STGC) is described in detail.展开更多
The rough set-genetic support vector machine(SVM) model is applied to supply chain performance evaluation. First, the rough set theory is used to remove the redundant factors that affect the performance evaluation of ...The rough set-genetic support vector machine(SVM) model is applied to supply chain performance evaluation. First, the rough set theory is used to remove the redundant factors that affect the performance evaluation of supply chain to obtain the core influencing factors. Then the support vector machine is used to extract the core influencing factors to predict the level of supply chain performance. In the process of SVM classification, the genetic algorithm is used to optimize the parameters of the SVM algorithm to obtain the best parameter model, and then the supply chain performance evaluation level is predicted. Finally, an example is used to predict this model, and compared with the result of using only rough set-support vector machine to predict. The results show that the method of rough set-genetic support vector machine can predict the level of supply chain performance more accurately and the prediction result is more realistic, which is a scientific and feasible method.展开更多
This paper proposes a novel approach, Markov Chain Monte Carlo (MCMC) sampling approximation, to deal with intractable high-dimension integral in the evidence framework applied to Support Vector Regression (SVR). Unli...This paper proposes a novel approach, Markov Chain Monte Carlo (MCMC) sampling approximation, to deal with intractable high-dimension integral in the evidence framework applied to Support Vector Regression (SVR). Unlike traditional variational or mean field method, the proposed approach follows the idea of MCMC, firstly draws some samples from the posterior distribution on SVR's weight vector, and then approximates the expected output integrals by finite sums. Experimental results show the proposed approach is feasible and robust to noise. It also shows the performance of proposed approach and Relevance Vector Machine (RVM) is comparable under the noise circumstances. They give better robustness compared to standard SVR.展开更多
文摘Agile Supply Chain Management (ASCM) is an important topic and has received much attention recently. ASCM is a new management technology. Agile Supply Chain Management Decision Support System (ASCM DSS) is presented. Firstly, agile supply chain management technology is introduced. Secondly a decision support system for agile supply chain management is proposed. Then, the implementation of ASCM DSS in enterprise is discussed. Finally, a fuzzy intelligence decision making process in Shanghai Turbine Generator Company (STGC) is described in detail.
文摘The rough set-genetic support vector machine(SVM) model is applied to supply chain performance evaluation. First, the rough set theory is used to remove the redundant factors that affect the performance evaluation of supply chain to obtain the core influencing factors. Then the support vector machine is used to extract the core influencing factors to predict the level of supply chain performance. In the process of SVM classification, the genetic algorithm is used to optimize the parameters of the SVM algorithm to obtain the best parameter model, and then the supply chain performance evaluation level is predicted. Finally, an example is used to predict this model, and compared with the result of using only rough set-support vector machine to predict. The results show that the method of rough set-genetic support vector machine can predict the level of supply chain performance more accurately and the prediction result is more realistic, which is a scientific and feasible method.
基金Supported by the National Natural Science Foundation of China (No. 60972106, 61072103)China Postdoctoral Science Foundation (No. 20090450750)
文摘This paper proposes a novel approach, Markov Chain Monte Carlo (MCMC) sampling approximation, to deal with intractable high-dimension integral in the evidence framework applied to Support Vector Regression (SVR). Unlike traditional variational or mean field method, the proposed approach follows the idea of MCMC, firstly draws some samples from the posterior distribution on SVR's weight vector, and then approximates the expected output integrals by finite sums. Experimental results show the proposed approach is feasible and robust to noise. It also shows the performance of proposed approach and Relevance Vector Machine (RVM) is comparable under the noise circumstances. They give better robustness compared to standard SVR.
