Fuzzy Shapley values are developed based on classical Shapley values and used to allocate profit among partners in virtual enterprises （VE）. Axioms of the classical Shapley value are extended to Shapley values with ...Fuzzy Shapley values are developed based on classical Shapley values and used to allocate profit among partners in virtual enterprises （VE）. Axioms of the classical Shapley value are extended to Shapley values with fuzzy payoffs by using fuzzy sets theory. Fuzzy Shapley function is defined based on these extended axioms. From the viewpoint the allocation for each partner should be a crisp value rather a fuzzy membership function at the end of cooperation, a crisp allocation scheme based on fuzzy Shapley values is proposed.展开更多
The fuzzy numerical value analysis method is adopted for the first time, which solves the problem of nanometer electro-thermal in filming process, The key technique is embodied by controlling the time distribution, te...The fuzzy numerical value analysis method is adopted for the first time, which solves the problem of nanometer electro-thermal in filming process, The key technique is embodied by controlling the time distribution, temperature and press in the filming process. The concrete technique of filming is showed by establishing the fuzzy mumbership function of above three indexes, which improves the precision of the materials of nanometer electro-thermal in hot-working. At the same time, the principles of the fuzzy relationship mapping inversion （FRMI） is put forward, Therefore, the standardization and continuity can be met.展开更多
A new incremental clustering method is presented, which partitions dynamic data sets by mapping data points in high dimension space into low dimension space based on （fuzzy） cross-entropy（CE）. This algorithm is di...A new incremental clustering method is presented, which partitions dynamic data sets by mapping data points in high dimension space into low dimension space based on （fuzzy） cross-entropy（CE）. This algorithm is divided into two parts： initial clustering process and incremental clustering process. The former calculates fuzzy cross-entropy or cross-entropy of one point relafive to others and a hierachical method based on cross-entropy is used for clustering static data sets. Moreover, it has the lower time complexity. The latter assigns new points to the suitable cluster by calculating membership of data point to existed centers based on the cross-entropy measure. Experimental compafisons show the proposed methood has lower time complexity than common methods in the large-scale data situations cr dynamic work environments.展开更多
Since collaborative, planning, forecasting, and replenishment （CPFR） was first proposed in 1998, numerous studies have focused on exploring its implementation in retailing contexts. While a considerable body of rese...Since collaborative, planning, forecasting, and replenishment （CPFR） was first proposed in 1998, numerous studies have focused on exploring its implementation in retailing contexts. While a considerable body of research has emphasized reduced costs, increased sales and improved forecasting ability, there has been a lack of research on the importance of each of the various factors which affect such implementations. In order to find out the critical success factors affecting CPFR implementation, this paper first collected related influence factors regarding adopting CPFR or business to business （B2B） information systems, and further constructed a factor table with a three-layer hierarchical structure. A pair wise analytic hierarchy process （AHP） questionnaire was designed and distributed to experts who were familiar with implementing CPFR in the retailing industry. After questionnaires were returned, we found out the weights of each impact factor by using a fuzzy analytic hierarchy process （fuzzy AHP） approach. The importance of each critical impact factor was investigated, and the paths of the critical success factors were also analyzed. The results of this study can provide more precise information with regard to allocating optimal resources for retailers implementing CPFR.展开更多
基金the National Natural Science Foundation of China (70471063 , 70171036)the Second Phase of "985"Project of China(107008200400024) the Main/Subject Project of Beijing of China(XK100070534)
文摘Fuzzy Shapley values are developed based on classical Shapley values and used to allocate profit among partners in virtual enterprises （VE）. Axioms of the classical Shapley value are extended to Shapley values with fuzzy payoffs by using fuzzy sets theory. Fuzzy Shapley function is defined based on these extended axioms. From the viewpoint the allocation for each partner should be a crisp value rather a fuzzy membership function at the end of cooperation, a crisp allocation scheme based on fuzzy Shapley values is proposed.
文摘The fuzzy numerical value analysis method is adopted for the first time, which solves the problem of nanometer electro-thermal in filming process, The key technique is embodied by controlling the time distribution, temperature and press in the filming process. The concrete technique of filming is showed by establishing the fuzzy mumbership function of above three indexes, which improves the precision of the materials of nanometer electro-thermal in hot-working. At the same time, the principles of the fuzzy relationship mapping inversion （FRMI） is put forward, Therefore, the standardization and continuity can be met.
文摘A new incremental clustering method is presented, which partitions dynamic data sets by mapping data points in high dimension space into low dimension space based on （fuzzy） cross-entropy（CE）. This algorithm is divided into two parts： initial clustering process and incremental clustering process. The former calculates fuzzy cross-entropy or cross-entropy of one point relafive to others and a hierachical method based on cross-entropy is used for clustering static data sets. Moreover, it has the lower time complexity. The latter assigns new points to the suitable cluster by calculating membership of data point to existed centers based on the cross-entropy measure. Experimental compafisons show the proposed methood has lower time complexity than common methods in the large-scale data situations cr dynamic work environments.
基金supported by the National Science Council (NSC) of the Executive Yuan, Taiwan. (NSC 97-2221-E-327-022)
文摘Since collaborative, planning, forecasting, and replenishment （CPFR） was first proposed in 1998, numerous studies have focused on exploring its implementation in retailing contexts. While a considerable body of research has emphasized reduced costs, increased sales and improved forecasting ability, there has been a lack of research on the importance of each of the various factors which affect such implementations. In order to find out the critical success factors affecting CPFR implementation, this paper first collected related influence factors regarding adopting CPFR or business to business （B2B） information systems, and further constructed a factor table with a three-layer hierarchical structure. A pair wise analytic hierarchy process （AHP） questionnaire was designed and distributed to experts who were familiar with implementing CPFR in the retailing industry. After questionnaires were returned, we found out the weights of each impact factor by using a fuzzy analytic hierarchy process （fuzzy AHP） approach. The importance of each critical impact factor was investigated, and the paths of the critical success factors were also analyzed. The results of this study can provide more precise information with regard to allocating optimal resources for retailers implementing CPFR.
