Study On Partner Selection Of Virtual Enterprise Based On Support Vector Machines

Under the competitive, collaborative and dynamic market circumstances, virtual enterprise is known as most competitive management mode of 21 -century. In fact, virtual enterprise is that the predominant corporation associates with other mutually beneficial partners in order to take hold of market opportunities in time. And it is very crucial that the predominant corporation can correctly select the partner from lots of potential partners. But at present people has not found a reasonable and effective method to help the predominant enterprises making decisions. So in the paper we will boldly adopt Support Vector Machines (SVM) algorithm to achieve partner selection.There are three kind of partners for the moment principally, such as, suppliers, producers and sellers. According to some principles of index, we firstly consider specific factors of influence on virtual enterprise partner selection. Then we respectively establish the three kind of partner selection evaluating index.Support Vector Machines is a new and very promising classification technique. The approach is systematic and properly motivated by statistical learning theory. Training involves separating the classes with a surface that maximizes the margin between them. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. Thus we put the theory and method of Support Vector Machines to apply partner selection in this thesis. Therefore, we must solve three related issues. The fist issue is how to evaluate by classifying. We concatenate the vectors of each two potential partners to be a "big" vector. We can classify such "big" vectors into three types, namely "better", "equal" and "worse", based on what relation between the two potential partners is. Thus we can tell the relation between any two partners by classifying the "big" vector concatenate from the vectors of them. The second issue is multi-class classification algorithms of Support Vector Machines. The traditional Support Vector Machines only deal with the binary classification. In this paper, based on four types multi-class classification algorithm, we deal with 3-class classification by one against one method, in which three machines are built to distinguish any two classes respectively. The third issue is how to form absolute evaluations based on the results of classification. To provide absolute evaluations, we adopt a round-robin-like mechanism, in which each partner is compared with every other one, and receive a mark based on the result. Such marks are cumulated to get the final evaluation of the partner. We have done lotsof simulation experiments. We have tried different kernels and adjusted parameters to find the best fit for our problem and built our system on it. In the last, we respectively apply our system on three samples in order to confirm that the system is effective.