Many prestigious researchers have been exploring the issues of imprecision, inconsistency, and uncertain information in decision making, which are still challenges in developing a decision support system that is more feasible and efficient. This chapter proposes a new multicriteria group decision-making (MCGDM) strategy to overcome those issues. This strategy integrates the original MABAC method with trapezoidal fuzzy neutrosophic numbers (TrFNNs). First, the proposed method converts independent judgments from experts in the form of linguistic variables into TrFNNs and aggregates them using some aggregation operators. Next, it utilizes the functions of score and accuracy to rank the evaluated alternatives. By using the distance measure between the alternatives and the border approximation area, the proposed MABAC selects the best solution. Finally, the chapter illustrates an example of COVID-19 vaccine selection and a comparative analysis to show that the proposed MABAC has benefits to support indeterminate information and is more reasonable and practicable in handling MCGDM problems.

The bipolar neutrosophic set is a suitable instrument to tackle the information with vagueness, complexity, and uncertainty. In this study, we improved the original EDAS (the evaluation based on distance from average solution) with bipolar neutrosophic numbers (BNNs) for a multiple-criteria group decision-making (MCGDM) problem. We calculated the average solution under all the criteria by two existing aggregation operators of BNNs. Then, we computed the positive distance and the negative distance from each alternative to the average ideal solution and determined the appraisal score of alternatives. Based on these scores, we obtained the ranking result. Finally, we demonstrated the practicability, stability, and capability of the improved EDAS method by analyzing the influence parameters and comparing results with an extended VIKOR method.

In this manuscript, we extend the traditional multi-attributive border approximation area comparison (MABAC) method for the multiple-criteria group decision-making (MCGDM) with triangular fuzzy neutrosophic numbers (TFNNs) to propose the TFNNs-MABAC method. In the proposed method, we utilize the TFNNs to express the values of criteria for each alternative in MCGDM problems. First, we briefly acquaint the basic concept of TFNNs and describe its corresponding some operation laws, the functions of score and accuracy, and the normalized hamming distance. We then review two aggregation operators of TFNNs. Afterward, we combine the traditional MABAC method with the triangular fuzzy neutrosophic evaluation and provide a sequence of calculation procedures of the TFNNs-MABAC method. After comparing it with some TFNNs aggregation operators and another method, the results showed that our extended MABAC method can not only effectively handle the conflicting attributes, but also practically deal with incomplete and indeterminate information in the MCGDM problem. Therefore, the extended MABAC method is more effective, conformable, and reasonable. Finally, an investment selection problem is demonstrated as a practice to verify the reasonability of our MABAC method.