A LSGDM method based on social network and IVIFN’s geometric characteristics for evaluating the collaborative innovation problem1

Abstract

During the development of regional economy, introducing collaborative innovation is an important policy. Constructing a scientific and effective measurement for evaluating the collaborative innovation degree is essential to determine an optimum collaborative innovation plan. As this problem is complex and has a long-lasting impact, this paper will propose a novel large scale group decision making (LSGDM) method both considering decision makers’ social network and their evaluation quality. Firstly, the decision makers will be detected based on their social connections and aggregated into different subgroups by an optimization algorithm. Secondly, decision makers are weighted according to their important degree and decision information, where the information is carried by interval valued intuitionistic fuzzy number (IVIFN). During the information processing, IVIFN is put in rectangular coordinate system considering its geometric meaning. And some related novel concept are given based on the barycenter of rectangle region determined by IVIFN. Meanwhile, the criteria’s weights are calculated by the accurate degree and deviation degree. A classical example is used to illustrate the effect of weighting methods. In summary, a large scale group decision making method based on the geometry characteristics of IVIFN (GIVIFN-LSGDM) is proposed. The scientific and practicability of GIVIFN-LSGDM method is illustrated through evaluating four different projects based on the constructed criteria system. Comparisons with the other methods are discussed, followed by conclusions and further research.

Two-sided matching based on I-BTM and LSGDM applied to high-level overseas talent and job fit problems

“…Different with Wu’s method, we measure the degree of intimacy based on the whole network structure. Zhang et al 47 used this measure to deal with the LSGDM problem during evaluating collaborative innovation degree. To prevent dense connections inside a group and sparse connections to the outside, we determine the subgroups’ weight vectors based on two aspects.…”