Improved TODIM Method Based on Linguistic Neutrosophic Numbers for Multicriteria Group Decision-Making

Peide Liu, Xinli You
2019 International Journal of Computational Intelligence Systems  
A B S T R A C T The TODIM (an acronym in Portuguese for interactive multicriteria decision-making) method can consider the decisionmakers' (DMs') psychological behavior. However, the classical TODIM method has been unable to address fuzzy information such as the linguistic neutrosophic number (LNN), which is an effective tool to represent uncertainty. In this paper, an extended TODIM method is proposed to solve multicriteria group decision-making (MCGDM) problems in a linguistic neutrosophic
more » ... tic neutrosophic environment. First, the definitions and characteristics of the classical TODIM and the LNNs are introduced. Then, an improved score function (SF) of LNNs is proposed. Furthermore, we obtain the combined weights of the criteria and aggregate individual decision matrices into a group decision matrix. The classical TODIM method is extended to address MCGDM problems with LNNs, and specific decision steps are provided. Finally, several examples are given to verify the effectiveness and superiority of the proposed approach by comparison with some existing methods. Rashno et al. utilized [11] NSs and graph algorithms to present a fully automated algorithm in the healthcare industry. Fan et al. proposed [12] a neutrosophic Hough transform (NHT) method to improve the track initiation monitoring capacity in an uncertain environment. In practice, people are used to giving their opinions in qualitative terms, such as "excellent, " "fair, " and "worse. " At this point, Zadeh defined [13] linguistic variables (LVs) to describe words or sentences in natural language. Since then, many studies on linguistic decision-making problems have been conducted. Wu et al. put forward [14] the maximum support degree model to guarantee the accuracy of group opinion based on linguistic distributions. Zhang et al. established [15] a new decision support model with 2-tuple linguistic terms that provided a basis for emergency decision-making. Furthermore, there are many extensions of LVs to accurately express evaluation information. Based on the FS model, Rodríguez et al. proposed [16] the concept of a hesitant fuzzy linguistic term set (HFLTS) in which a DM may hesitate among several LVs to define the TM. Analogously, Chen and Liu defined [17] the linguistic intuitionistic fuzzy numbers (LIFNs) that represent the TM and FM by LVs. However, the above linguistic forms reflect only the TM (and FM), and thus they are insufficient to accommodate uncertain and inconsistent information. Therefore, Fang and Ye introduced [18] the concept of linguistic neutrosophic numbers (LNNs) based on NS, which is characterized by describing the TM, IM, and FM of each element in a universe using three LVs. For MCGDM problems, there are two common methods to help DMs select the optimal proposal from a variety of alternatives. One method uses aggregation operators that integrate the evaluation Pdf_Folio:1
doi:10.2991/ijcis.d.190412.001 fatcat:yhnhcex76ngmjoelncve3qzwwa