Behavior analysis of credit cardholders is one of the main research topics in credit card portfolio management. Usually, the cardholder's behavior, especially bankruptcy, is measured by a score of aggregate attributes that describe cardholder's spending history. In real-life practice, statistics and neural networks are the major players to calculate such a score system for prediction. Recently, various multiple linear programming-based classification methods have been promoted for analyzing credit cardholders' behaviors. As a continuation of this research direction, this paper proposes a heuristic classification method by using the fuzzy linear programming (FLP) to discover the bankruptcy patterns of credit cardholders. Instead of identifying a compromise solution for the separation of credit cardholder behaviors, this approach classifies the credit cardholder behaviors by seeking a fuzzy (satisfying) solution obtained from a fuzzy linear program. In this paper, a real-life credit database from a major US bank is used for empirical study which is compared with the results of known multiple linear programming approaches.
Taking the theses' keywords in China from 1986 to 2014 as the research materials, use the basis concept of the Big Data Theory to further study the keywords which related to oil and gas industry. Analyze the keywords frequency of the theses in oil and gas industry and its co-occurrence frequency pair, and then use the theory of mapping knowledge domain to visualize the keywords co-occurrence network in petroleum industry so as to make further research of the heated issues that mapping knowledge domain has shown. According to the research we can see that the application technology R&D (research and development) predominate the oil and gas industry, featuring a high concentration and long tail phenomenon (which means various researches focus on different kinds of things, the scale of the research is large).
This article investigates an optimal operation model which is based on improved genetic algorithm for natural gas pipeline network. First, the maximum benefit and the maximum flow were chosen as the objective function, and several conditions were selected as the constraints including the input and output of gas, the input and output pressure of gas, the handling capacity of compressive station, the strength of the pipeline, decreasing of the pipeline pressure, the compressor, the valve, and the flow balance of pipe network node. On the basis of the above two aspects, the optimal mathematical operation model of natural gas pipeline network is established. Second, an improved genetic algorithm is proposed due to the possibility that the fitness value of particular individual in the initial population is abnormal and the possibility that the probabilities of the crossover and the mutation are too high or too low. Finally, a medium-pressure pipe network is taken as a study example. Compared with the basic genetic algorithm and the non-optimized genetic algorithm, the maximum benefit and maximum flow rate of the improved genetic algorithm are increased by 3.09%, 1.61%, 5.98%, and 2.44%, respectively.
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