Bilateral matching decision-making for knowledge innovation management considering matching willingness in an interval intuitionistic fuzzy set environment

Based on the real-world knowledge innovation management characteristics of enterprises, in this paper a novel bilateral matching (BM) decision-making method for knowledge innovation management considering the matching willingness of bilateral enterprises is proposed. The method uses interval-valued intuitionistic fuzzy sets (IvIFSs) as its basis. First, using the IvIFS preferences of the bilateral enterprises, their matching willingness is calculated according to the TOPSIS method. Then, the BM model is constructed according to the IvIFS preference, the matching willingness and the BM matrix of the bilateral enterprises. According to the normalized interval-valued score function (NIvSF) and score function (SF), the BM model is transformed into a BM model with SFs. Considering the fairness of each agent of each side, the BM model with SFs is transformed into a two-objective BM model. Furthermore, a novel optimization algorithm is introduced to solve the two-objective model, and then the optimal BM scheme is obtained. Finally, the effectiveness and feasibility of the proposed method are verified by a knowledge innovation management case study. The key findings of the proposed work are as follows: (1) The proposed method establishes the BM model with IvIFSs and matching willingness; (2) a new algorithm for the BM model is developed; and (3) the obtained BM scheme using the proposed method reflects the matching willingness of the agents. The proposed method can be extended to other BM problems in knowledge innovation management operating under other intuitionistic fuzzy environments and can be applied to other decision-making problems in enterprise knowledge innovation management.