The purpose of this paper is to develop an efficient identification technique for 2-additive fuzzy measures with a deep understanding of objective relationships among features. More efforts are made to achieve this goal. Firstly, an efficient form of monotonicity constraints is presented for the 2-additive fuzzy measure, thus reducing the sheer number of monotonicity constraints from \(n{2}^{n-1}\) down to \(n\left(n-1\right)/2\) for \(n\) features or decision criteria. By this means, we can obtain the two kinds of index values more efficiently, i.e., the importance index values of decision criteria and the interaction index values between them, especially the latter. Secondly, a decision parameter is introduced to reinforce the decision-making trial and evaluation laboratory method. In this way, the objective coefficients are specifically designed, with a deep understanding of the objective cause-and-effect relationships among criteria, to adjust the initial index values as above with the aim of reducing the decision group’s subjective bias. A desired monotone 2-additive fuzzy measure can then be identified by using these two kinds of index values adjusted. Finally, the developed technique is applied to the green material selection with a practical example, and the numerical efficiency shows that in the identification of 2-additive fuzzy measures with 6 features, the number of monotonicity constraints massively reduces from the original 192 down to the current 15. We also show the relatively good performance of the developed technique by comparing it with other methods.
