Abstract
The probabilistic linguistic term set (PLTS) is an effective tool for describing linguistic value in the process of decision-making. However, due to its complex structure, there remains numerous defects in utilizing the PLTS information. Therefore, by connecting the PLTS with discrete probability distribution, this study investigates a novel probability distribution-based processing model to process PLTS and further develops a practical multi-criteria decision-making (MCDM) approach for addressing automatic environmental monitoring evaluation problem. First, we propose two types of pre-processing methods to complete probability normalization. Accordingly, the normalized PLTS is represented in the form of probabilistic linguistic probability distribution (PLPD). Then, probabilistic linguistic earth-mover (PLEM) distance is constructed to measure the divergency between PLPDs, and a probabilistic linguistic probability distribution weighted mean (PLPDWM) is also developed to facilitate information fusion. On this basis, an aggregation operator-based MCDM approach is established that integrates the probability distribution-based PLTS processing methods and the proposed worst-priority weight (WPW) method. Finally, the developed approach is demonstrated by solving a practical atmospheric pollutant evaluation problem and its strengths are verified through further sensitivity analysis and comparative analysis.
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