Building a good supply network has a competitive advantage in every part of business. However, people rarely know the principles from which supply chain with complex organizational structure and function arise and develop. In this paper, we develop an evolving supply network model by using complex network theory. We mainly consider three kinds of firms＇ behaviors： entering of new firms, adding new relationships and rewiring of relationships among firms. By analyzing the statistical characteristics of the evolutionary dynamics of supply network, we find that the degree distribution follows a power-law distribution. Therefore, a supply network is a scale-free network where few but significant firms have lots of connections （called ＂hub＂ or core firm）, while most firms have few connections. These results are consistent with the results in empirical researches, which will be very useful for designing a robust and effective supply network.
This paper develops goal programming algorithm to solve a type of least absolute value (LAV) problem. Firstly, we simplify the simplex algorithm by proving the existence of solutions of the problem. Then, we present a goal programming algorithm on the basis of the original techniques. Theoretical analysis and numerical results indicate that the new method contains a lower number of deviation variables and consumes less computational time as compared to current LAV methods.
This paper mainly investigates the asymmetry of the conditional volatility of Chinese stock market by using the GARCH models. We collect the dally data of Shanghai composite index to analyze the volatility asymmetry. The empirical results show that there exists a distinct volatility asymmetry for return. In addition, we extend G JR model to the case with the information flow that is represented by the volume, and the results imply that the volume can＇t substitute the information flow to account for the conditional volatility asymmetry.
