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Hangzhou, China

Hu J.-S.,Qingdao University | Xu Y.-J.,Agricultural Bank of China | Liu F.-X.,Qingdao University | Wang C.-C.,Qingdao University
Kongzhi yu Juece/Control and Decision | Year: 2012

A competitive supply chain network equilibrium model with multi-differentiated products is studied, which comprises noncooperative manufactures and retailers with fuzzy market demand. Multinomial logit model is used to analyze consumer's stochastic choice of multi-differentiated products in order to describe the consumer's preference. The expected profit of retailers is derived by credibility measure of fuzzy event. The supply chain network is developed based on finite-dimensional variational inequalities associated with the various decision makers. Existence and uniqueness of the supply chain network equilibrium are analyzed in detail. Finally, the impacts of fuzzy demand on supply chain network equilibrium are illustrated through numerical examples. Source


Yang C.G.,Chinese Electronic Equipment System Corporation Institute | Liu Q.G.,Agricultural Bank of China
Advanced Materials Research | Year: 2014

Marriage in Honey Bees Optimization (MBO) is a new swarm-intelligence method, but it has the shortcomings of low speed and complex computation process. By changing the structure of MBO and utilizing a linear method to perform the local characteristic, we propose a new optimization algorithm. The global convergence characteristic of the proposed algorithm is proved by using the Markov Chain theory. And then some simulations are done on Traveling Salesman Problem (TSP) and several public evaluation functions. By comparing the proposed algorithm with MBO and Genetic Algorithm, simulation results show that the proposed algorithm has better convergence performance. © (2014) Trans Tech Publications, Switzerland. Source


Kiang M.Y.,California State University, Long Beach | Ye Q.,Harbin Institute of Technology | Hao Y.,Agricultural Bank of China | Chen M.,California State University, Channel Islands | Li Y.,Harbin Institute of Technology
Decision Support Systems | Year: 2011

Studies of Internet market have found that consumers' purchasing behaviors including information search, channel selection, and brand evaluation processes are impacted by the product/service types. In this research, we perform a comprehensive review of existing online product classification methods. An in-depth analysis of the rationale behind each method was studied, and important product characteristics were determined for integrating different approaches. Grounded on the theory of the components of perceived risk in product purchase and the recently emerged service science, the integrated classification will help companies to better understand the online consumer purchasing behavior and to design their e-commerce strategies accordingly. Survey results indicate that the integrated classification is an effective way to classify products and services marketed on the Internet. © 2011 Elsevier B.V. All rights reserved. Source


Tang L.,Beijing University of Chemical Technology | Wang C.,Agricultural Bank of China | Wang S.,CAS Academy of Mathematics and Systems Science
Procedia Computer Science | Year: 2013

This paper attempts to propose an integrated data characteristic testing approach for energy time series data so as to analyze the energy dynamics, which serves as the foundation for the model selection problem. Based on thoroughly analyzing the main data characteristics of energy time series data together with their interrelationship, these data characteristics are divided into two main categories: nature and pattern characteristics to explore energy time series data from different perspectives. In nature determination, the energy time series data is analyzed in terms of nonstationarity, nonlinearity and complexity characteristics from a global perspective. In pattern measurements, the characteristics of cyclicity (and seasonality), mutability (or saltation) and randomicity (or noise pattern) signify the relative hidden patterns and the impacts on the original data, via a way of decomposition. For illustration purpose, hydropower consumptions in China and USA are analyzed and the main data characteristics are thoroughly explored by using the proposed integrated approach. Empirical results reveal that besides same characteristics of difference strationarity, nonlinearity and seasonality, the hydropower markets in China and USA are quite different: while China's hydropower market are comparatively simple but sensitive to emergencies, e.g., government support and technological progress, US' hydropower market is otherwise mature and efficient with the nature of high leveled complexity and the main pattern of randomicity. The results also confirm the proposed integrated approach an effective tool to test energy time series data in terms of data characteristics, paving the way for the further model formulation and forecasting. © 2013 The Authors. Published by Elsevier B.V. Source


Yang J.,Peking University | Wang Z.,Agricultural Bank of China | Liu X.,Wuhan University of Science and Technology | Tan S.,Peking University
Journal of Computational Information Systems | Year: 2014

In this paper we propose an improved twofold method to identify stock groups using the correlation-based network on stock return time series. First, a denoising method, namely random matrix theory, is used to construct the correlation matrix of stock groups, in which nontrivial high correlations between stocks are computed by filtering out both random noise and common trend. Second, group identification of stock network is modeled as a multi-objective optimization problem and solved by the weighted version of multi-objective genetic algorithm (MOGA). An effective population initialization scheme is developed and genetic operators are improved to reduce the effective search space. In experiments, we have used 166 stocks from the Chinese stock market for the year 2009-2011 and the effectiveness of the proposed method is shown compared to the contestant algorithms. And also we have constructed portfolios by selecting stocks from the clusters based on highest eigenvector centrality together with highest expected return, and compared the returns with that of the benchmark index, such as HS300. © 2014 Binary Information Press. Source

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