Research On The Comprehensive Evaluation Model In The Partner Selection Of Virtual Enterprises

With the increasingly globalization of marketing competition and requirement of customer's diversification and individuation, Virtual Enterprises (VE) gradually become a new form of enterprises for achieving the competitive strength and gaining some market opportunity, and become the hot topic in industrial and academic area. The key of virtual enterprises success is the choice of right partner, so research on the selecting partner of VE is very important.Firstly, the paper discusses the background, the concept and the features about the VE and the academic basis of virtual enterprises'foundation. At the same time, the dissertation analyses and summarizes the existing methods of partner selection, and point out their disadvantages. Then a new extensive comprehensive partner selection method is proposed, and the theory used in the method, such as Extension, Artificial Neural Network (ANN) and Genetic Algorithm (GA), are introduced. Secondly, to evaluate partners comprehensively and objectively, a general partner comprehensive evaluation system is defined. Then the paper emphases on how to select the partner of VE and uses a three phases model to select the partner. The first phase it uses the rough selection to confirm the range of partner. The second phase is subtle selection, which uses the ANN to get the weight of the evaluation indexes and uses the Extension to select the right partner from the candidates on considering the factors that influence the decision. The last phases uses the GA to combine the partner for ensuring the strength of VE.Finally, the paper designs and emulates the comprehensive selection model based on the plat of matlab. With the help of the emulation, the paper illustrates the process of selecting the partner and validates the method's practicability as well. The result shows that the model improves the effect and efficiency of the Virtual enterprises'Partner selection and comprehensive evaluation approach in the comparison with the results of other choosing models.