Abstract
Coal mine safety has been a pressing issue for many years, and it is a constant and non-negligible problem that must be addressed during any coal mining process. This paper focuses on developing an innovative multi-criteria decision-making (MCDM) method to address coal mine safety evaluation problems. Because lots of uncertain and fuzzy information exists in the process of evaluating coal mine safety, linguistic intuitionistic fuzzy numbers (LIFNs) are introduced to depict the evaluation information necessary to the process. Furthermore, the handling of qualitative information requires the effective support of quantitative tools, and the linguistic scale function (LSF) is therefore employed to deal with linguistic intuitionistic information. First, the distance, a valid ranking method, and Frank operations are proposed for LIFNs. Subsequently, the linguistic intuitionistic fuzzy Frank improved weighted Heronian mean (LIFFIWHM) operator is developed. Then, a linguistic intuitionistic MCDM method for coal mine safety evaluation is constructed based on the developed operator. Finally, an illustrative example is provided to demonstrate the proposed method, and its feasibility and validity are further verified by a sensitivity analysis and comparison with other existing methods.
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Acknowledgements
The authors thank the editors and anonymous reviewers for their very helpful comments and suggestions. This work was supported by the National Natural Science Foundation of China (Nos. 71571193 and 71271218) and the Fundamental Research Funds for the Central Universities of Central South University (No. 2016zzts213).
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Appendix
Appendix
Proof of Theorem.
In the following, Theorem 1 will be proved utilizing the mathematical induction on \(n\).
Proof
Firstly, the following equation needs to be proved.
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1.
For n = 2, the following equation can be calculated easily.
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2.
If Eq. (11) holds for \(n=k\), there is
Then, when \(n=k+1\), the following equation can be obtained
According to the operations of LIFNs, the following result can be calculated.
Equation (15) can be easily proved by utilizing the mathematical induction on \(k+1\), and the proof is omitted here.
Thus, by utilizing Eqs. (13) and (15), Eq. (14) can be converted into
That is, Eq. (12) also holds for \(n=k+1.\) Thus, Eq. (12) is true for all \(n\).
Then, by using Eq. (12), Eq. (6) can be calculated easily based on the operations of LIFNs, and Eq. (7) can be eventually acquired. Therefore, the proof Theorem 1 is completed.
Proof of Theorem 3.
Proof
Let \(LIFFIWH{{M}^{p,q}}({{a}_{1}},{{a}_{2}},...,{{a}_{n}})=({{s}_{u(a)}},{{s}_{v(a)}})\)and \(LIFFIWH{{M}^{p,q}}({{b}_{1}},{{b}_{2}},...,{{b}_{n}})=({{s}_{u(b)}},{{s}_{v(b)}})\). Since \({{f}^{*}}\), \({{f}^{*-1}}\) and \({{\log }_{\lambda }},(\lambda>1)\) is a strictly monotonously increasing and continuous function, and \({{s}_{u({{a}_{i}})}}\ge {{s}_{u({{b}_{i}})}}\) and \({{s}_{u({{a}_{j}})}}\ge {{s}_{u({{b}_{j}})}}\) for all \(i,j=1,2,...,n\), then the following inequalities can be obtained.
In the same way, the inequality \({{s}_{v(a)}}\le {{s}_{v(b)}}\) can also be obtained. Since \({{s}_{u(a)}}\ge {{s}_{u(b)}}\) and \({{s}_{v(a)}}\le {{s}_{v(b)}}\), then, \(LIFFIWH{{M}^{p,q}}\left( {{a}_{1}},{{a}_{2}},...,{{a}_{n}} \right)\ge LIFFIWH{{M}^{p,q}}\left( {{b}_{1}},{{b}_{2}},...,{{b}_{n}} \right).\)
Proof of Theorem 4.
Proof
Since \({{s}_{u(a)}}\ge {{s}_{{{u}_{i}}}}\) and \({{s}_{v(a)}}\le {{s}_{{{v}_{i}}}}\) for all \(i=1,2,...,n,\) then according to Theorems 2 and 3, we can obtain\(LIFFIWH{{M}^{p,q}}({{\phi }_{1}},{{\phi }_{2}},...,{{\phi }_{n}})\le LIFFIWH{{M}^{p,q}}(a,a,...,a)=a.\) Since \({{s}_{u(b)}}\le {{s}_{{{u}_{i}}}}\) and \({{s}_{v(b)}}\ge {{s}_{{{v}_{i}}}}\) for all \(i=1,2,...,n\), then we can also obtain \(b=LIFFIWH{{M}^{p,q}}(b,b,...,b)\le LIFFIWH{{M}^{p,q}}({{\phi }_{1}},{{\phi }_{2}},...,{{\phi }_{n}})\). Thus, \(b\le LIFFIWH{{M}^{p,q}}({{\phi }_{1}},{{\phi }_{2}},...,{{\phi }_{n}})\le a\) is true.
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Peng, Hg., Wang, Jq. & Cheng, Pf. A linguistic intuitionistic multi-criteria decision-making method based on the Frank Heronian mean operator and its application in evaluating coal mine safety. Int. J. Mach. Learn. & Cyber. 9, 1053–1068 (2018). https://doi.org/10.1007/s13042-016-0630-z
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DOI: https://doi.org/10.1007/s13042-016-0630-z