Abstract
The notable characteristic of large-scale linguistic decision-making problems is that there are so many decision makers who provide linguistic assessments by using fuzzy linguistic representation models. In real-world applications, fuzzy linguistic terms mean different things for different people, and linguistic assessments based on different linguistic representation models may be simultaneous in the same large-scale linguistic decision-making problems. To this end, a novel linguistic decision-making method based on the voting model is proposed in the paper to deal with multi-linguistic assessments provided by decision makers. In large-scale linguistic decision process, evaluation-based voting is defined and multi-linguistic decision matrix is designed to represent multi-linguistic assessments provided by decision makers by using different linguistic representation models, and properties of the decision matrix are analyzed to show that linguistic assessments based on different linguistic representation models can be simultaneously represented. Based on multi-linguistic decision matrix, a new linguistic decision-making framework is developed to deal with large-scale linguistic decision-making problems with multi-linguistic assessments, in which normalization of multi-linguistic decision matrix and trust degrees of linguistic terms are contained, and more important, based on trust degrees of linguistic terms and 2-tuple fuzzy linguistic aggregation operators, an uniform fusion method of multi-linguistic assessments is proposed to aggregate multi-linguistic assessments of large-scale linguistic decision-making problems. Finally, user experiences of shared bikes, which are a large-scale linguistic decision-making problem in real-world applications, are employed to show the new decision-making framework and the uniform fusion method of multi-linguistic assessments, and furthermore, compared with existing linguistic decision-making methods analyzed in the example, it seems that multi-linguistic decision matrix and the uniform fusion method are useful and effective tools to deal with large-scale linguistic decision-making problems with multi-linguistic assessments.
Highlights
For any decision-making problems, how to reasonably evaluate and express assessments of alternatives with respect to criteria is the first and important step
The situation is very common in large-scale linguistic decision environments, such as in social networks and e-democracy, huge amounts of users take part in decision making and provide linguistic assessments of alternatives based on fuzzy linguistic representation models (Ding and Palomares 2020; Zhong and Xu 2020; Zhang et al 2017; Labella et al 2018; Rodriguez et al 2018), and multi-linguistic assessments instead of one kind of linguistic assessments may be more suitable to large-scale linguistic decision-making problems (LS-MLDM)
Compared with LS-MLDM problems, because there are a huge amounts of decision makers and fuzzy linguistic terms mean different things for different decision makers, in decision process different fuzzy linguistic representation models may be employed by decision makers to assess alternatives with respect to criteria, a new linguistic decision-making framework of LS-MLDM problems is designed in the paper, which is pictured in Fig. 2 and mainly consisted of the following five phases: framework
Summary
For any decision-making problems, how to reasonably evaluate and express assessments of alternatives with respect to criteria is the first and important step. 2-TLM, HFLTS, PLTS and FLM-DFN aim to express different imprecise or uncertain linguistic information, provide us tools to more reasonable express linguistic assessments of alternatives than using single-linguistic term in S = {s0, · · · , sg} and obtain acceptable decision-making results. They produce a significant challenge in decision-making process. For a LDM problem, because fuzzy linguistic terms mean different things for different decision makers, decision maker di maybe provide single-linguistic assessments of alternatives, but decision maker di (i = i) maybe utilize 2-TLM, HFLTS, PLTS or FLM-DFN to assess alternatives according to his/her knowledge level (social context or experience); this means that multi-linguistic assessments based on these linguistic representation models are simultaneous in the LDM problem. The situation is very common in large-scale linguistic decision environments, such as in social networks and e-democracy, huge amounts of users take part in decision making and provide linguistic assessments of alternatives based on fuzzy linguistic representation models (Ding and Palomares 2020; Zhong and Xu 2020; Zhang et al 2017; Labella et al 2018; Rodriguez et al 2018), and multi-linguistic assessments instead of one kind of linguistic assessments may be more suitable to large-scale linguistic decision-making problems (LS-MLDM)
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