Weighted Portfolio Selection Models Based on Possibility TheoryChen, Wei
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0010-4
AbstractIn this paper, we discuss portfolio selection problem in a fuzzy uncertain environment. Based on the Fullér's and Zhang's notations, we discuss some properties of weighted lower and upper possibilistic means and variances as in probability theory. We further present two weighted possibilistic portfolio selection models with bounded constraint, which can be transformed to linear programming problems under the assumption that the returns of assets are trapezoidal fuzzy numbers. At last, a numerical example is given to illustrate our proposed effective means and approaches.
Agile Infrastructure Design for Garment IndustrySu, Jin-long; Ouyang, Zhong-hui; Ge, Wan-cheng
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0011-3
AbstractThe Information Economics and its platform can input the vigor into manufacturing industry, i.e., garment industry, help to increase its ability for good return, promote the level of technology and management of Garments from a labor-intensive industry, and move it to the accurate management. The platform is designed as Comprehensive Information Platform by our research team. This paper is a general report series of papers the design of Agile Infrastructure for Collaborative Manufacturing and Agile Supply Chain. The key enabled technologies in the platform includes agile infrastructure and its application, business resources planning in manufacturing system, the design of data access model, optimal strategy for production planning, the dynamic load balance scheduling model, the intrusion detection technology and intelligent communication strategy. All of them are elaborated in turn. The paper tries to introduce the research of Agile Infrastructure and its key technologies, showing how well they work in some traditional manual industries.
A Survey of Fuzzy Decision Tree ClassifierChen, Yi-lai; Wang, Tao; Wang, Ben-sheng; Li, Zhou-jun
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0012-2
AbstractDecision-tree algorithm provides one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data, and with missing or noisy features. Recently, with the growing popularity of fuzzy representation, some researchers have proposed to utilize fuzzy representation in decision trees to deal with similar situations. This paper presents a survey of current methods for Fuzzy Decision Tree (FDT) designment and the various existing issues. After considering potential advantages of FDT classifiers over traditional decision tree classifiers, we discuss the subjects of FDT including attribute selection criteria, inference for decision assignment and stopping criteria. To be best of our knowledge, this is the first overview of fuzzy decision tree classifier.
Some Single-machine Scheduling Problems with Actual Time and Position Dependent Learning EffectsSun, Kai-biao; Li, Hong-xing
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0013-1
AbstractIn this paper we study some single-machine scheduling problems with learning effects where the actual processing time of a job serves as a function of the total actual processing times of the jobs already processed and of its scheduled position. We show by examples that the optimal schedules for the classical version of problems are not optimal under this actual time and position dependent learning effect model for the following objectives: makespan, sum of kth power of the completion times, total weighted completion times, maximum lateness and number of tardy jobs. But under certain conditions, we show that the shortest processing time (SPT) rule, the weighted shortest processing time (WSPT) rule, the earliest due date (EDD) rule and the modified Moore's Algorithm can also construct an optimal schedule for the problem of minimizing these objective functions, respectively.
Application of System NCF Method to Ice Flood Prediction of the Yellow RiverGuo, Yu
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0015-z
AbstractCombined forecasts is a well-established procedure for improving forecasting accuracy which takes advantage of the availability of both multiple information and computing resources for data-intensive forecasting. Therefore, based on the combination of engineering fuzzy set theory and artificial neural network theory as well as genetic algorithms and combined forecast theory, the system Non-linear Combined Forecast (NCF) method is established for accuracy enhancement of prediction, especially of ice flood prediction. The NCF values from single forecast model for Inner Mongolia Reach of the Yellow River are given. The case shows that the method has clear physical meanings and precise consequences. Compared with any single model, the system NCF method is more rational, effective and accurate.