Mine safety evaluation method using correlation coefficients of consistency linguistic neutrosophic sets in a linguistic neutrosophic multivalued environment.

Mine safety evaluation (MSE) is a complicated engineering problem that may contain the truth, falsity, and indeterminacy information. In the process of mine safety evaluation, the linguistic neutrosophic multivalued information can conveniently express the truth, falsity, and indeterminacy linguistic values and satisfy the thinking and expression habits of decision makers. Then, existing probabilistic linguistic neutrosophic decision-making methods are difficult to reveal the real probability values in several linguistic evaluation values. To reasonably simplify the expression and operation of the linguistic neutrosophic multivalued information and overcome the defects of the probability methods, the aim of this study is to propose a safety evaluation method of mines using correlation coefficients of consistency linguistic neutrosophic sets (CLNSs) in the setting of linguistic neutrosophic multivalued sets (LNMVSs). To do so, this study first defines LNMVS based on truth, falsity, and indeterminacy linguistic multivalued sequences with identical and/or different linguistic values, and then proposes a transformation method from LNMVS to CLNS based on the average values and consistency degrees (complement of standard deviation) of the linguistic multivalued sequences to reasonably simplify the information expression and operations of LNMVSs. Next, correlation coefficients between CLNSs and their MSE method are developed to solve MSE problems. By an actual example about the safety assessment of metal mines and relative comparative analysis, the evaluated results reveal that the proposed MSE method can reasonably and effectively deal with safety evaluation problems of the mines in the setting of LNMVSs. The proposed method can not only reflect the mean and consistency information of multiple language evaluation values and overcome the defect of unreasonable information generated by the probabilistic language method in small evaluation data, but also make evaluation results more effective and credible. [ABSTRACT FROM AUTHOR]