Integrating BWM and ARAS under hesitant linguistic environment for digital supply chain finance supplier section
Abstract
In the era of intelligence and informatization, digital supply chain finance (DSCF) has become one of the important trends in the development of supply chain finance. With the gradual increase of DSCF suppliers and various requirements of small and medium-sized enterprises for suppliers in providing financing services, selecting the most suitable DSCF supplier is of great significance for most small and medium-sized enterprises to expand reproduction and improve competitiveness. To address such a decision-making problem, this paper proposes a new multi-expert multiple criteria decision-making model by integrating the Best Worst Method (BWM) and Additive Ratio ASsessment (ARAS) method under the hesitant fuzzy linguistic environment, in which the hesitant fuzzy linguistic BWM method is applied to determine the weights of criteria while the hesitant fuzzy linguistic ARAS method is proposed to rank the candidate suppliers. A case study is given to demonstrate the procedure of the proposed method for the selection of optimal DSCF suppliers, which shows the feasibility of the proposed method. Finally, sensitivity analysis and comparative analyses are provided to testify the applicability and superiority of the proposed method.
Keyword : digital supply chain finance, hesitant fuzzy linguistic terms set, best worst method, additive ratio assessment, supplier selection, multiple criteria decision making
How to Cite
Liao, H., Wen, Z., & Liu, L. (2019). Integrating BWM and ARAS under hesitant linguistic environment for digital supply chain finance supplier section. Technological and Economic Development of Economy, 25(6), 1188-1212. https://doi.org/10.3846/tede.2019.10716
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Beg, I., & Rashid, T. (2013). TOPSIS for hesitant fuzzy linguistic term sets. International Journal of Intelligent Systems, 28, 1162-1171. https://doi.org/10.1002/int.21623
Büyüközkan, G., & Göcer, F. (2018). An extension of ARAS methodology under interval valued intuitionistic fuzzy environment for digital supply chain. Applied Soft Computing, 69, 634-654. https://doi.org/10.1016/j.asoc.2018.04.040
Dahooie, J. H., Zavadskas, E. K., Abolhasani, M., Vanaki, A., & Turskis, Z. (2018). A novel approach for evaluation of projects using an interval-valued fuzzy additive ratio assessment (ARAS) method: a case study of oil and gas well drilling projects. Symmetry, 10(2), 45. https://doi.org/10.3390/sym10020045
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Ghadikolaei, A. S., Madhoushi, M., & Divsalar, M. (2018). Extension of the VIKOR method for group decision making with extended hesitant fuzzy linguistic information. Neural Computing and Applications, 30(12), 3589-3602. https://doi.org/10.1007/s00521-017-2944-5
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Karabasevic, D., Zavadskas, E. K., Turskis, Z., & Stanujkic, D. (2016). The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties. Informatica, 27, 49-65. https://doi.org/10.15388/Informatica.2016.76
Keršulienė, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243-258. https://doi.org/10.3846/jbem.2010.12
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Liao, H. C., Qin, R., Gao, C. Y., Wu, X.L., Hafezalkotob, A., & Herrera, F. (2019b). Score-HeDLiSF: A score function of hesitant fuzzy linguistic term set based on hesitant degrees and linguistic scale functions: An application to unbalanced hesitant fuzzy linguistic MULTIMOORA, Information Fusion, 48, 39-54. https://doi.org/10.1016/j.inffus.2018.08.006
Liao, H. C., Xu, Z. S., Herrera-Viedma, E., & Herrera, F. (2018). Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the-art survey. International Journal of Fuzzy Systems, 20(7), 2084-2110. https://doi.org/10.1007/s40815-017-0432-9
Liao, H. C., Xu, Z. S., & Zeng, X. J. (2015a). Hesitant fuzzy linguistic VIKOR method and its application in qualitative multiple criteria decision making. IEEE Transactions on Fuzzy Systems, 23(5), 1343-1355. https://doi.org/10.1109/TFUZZ.2014.2360556
Liao, H. C., Xu, Z. S., Zeng, X. J., & Merigó, J. M. (2015b). Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowledge-Based Systems, 76, 127-138. https://doi.org/10.1016/j.knosys.2014.12.009
Mardani, A., Jusoh, A., Halicka, K., Ejdys, J., Magruk, A., & Ahmad, U. N. U. (2018). Determining the utility in management by using multi-criteria decision support tools: a review. Economic ResearchEkonomska Istraživanja, 31, 1666-1716. https://doi.org/10.1080/1331677X.2018.1488600
Mi, X. M., Tang, M., Liao, H. C., Shen, W. J., & Lev, B. (2019a). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega, 87, 205-225. https://doi.org/10.1016/j.omega.2019.01.009
Mi, X. M., Wu, X. L., Tang, M., Liao, H. C., Al-Barakati, A., Altalhi, A. H., & Herrera, F. (2019b). Hesitant fuzzy linguistic analytic hierarchical process with prioritization, consistency checking and inconsistency repairing. IEEE Access, 7(1), 44135-44149. https://doi.org/10.1109/ACCESS.2019.2908701
Mou, Q., Xu, Z. S., & Liao, H. C. (2016). An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences, 374, 224-239. https://doi.org/10.1016/j.ins.2016.08.074
Radović, D., Stević, Ž., Pamučar, D., Zavadskas, E. K., Badi, I., Antucheviciene, J., & Turskis, Z. (2018). Measuring performance in transportation companies in developing countries: a novel rough ARAS model. Symmetry, 10, 1-24. https://doi.org/10.3390/sym10100434
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
Rodríguez, R. M., Martinez, L., & Herrera, F. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1), 109-119. https://doi.org/10.1109/TFUZZ.2011.2170076
Shi, L. L., & Ye, J. (2017). Cosine measures of linguistic neutrosophic numbers and their application in multiple attribute group decision-making. Information, 8(4), 117. https://doi.org/10.3390/info8040117
Sremac, S., Stević, Ž., Pamučar, D., Arsić, M., & Matić, B. (2018). Evaluation of a third-party logistics (3PL) provider using a rough SWARA–WASPAS model based on a new rough dombi aggregator. Symmetry, 10(8), 305. https://doi.org/10.3390/sym10080305
Stanujkic, D., Zavadskas, E. K., Karabasevic, D., Turskis, Z., & Keršulienė, V. (2017). New group decision-making ARCAS approach based on the integration of the SWARA and the ARAS methods adapted for negotiations. Journal of Business Economics & Management, 18, 599-618. https://doi.org/10.3846/16111699.2017.1327455
Stević, Ž., Pamučar, D., Zavadskas, E. K., Ćirović, G., & Prentkovskis, O. (2017). The selection of wagons for the internal transport of a logistics company: A novel approach based on rough BWM and rough SAW methods. Symmetry, 9(11), 264. https://doi.org/10.3390/sym9110264
Sugihara, K., Ishii, H., & Tanaka, H. (2004). Interval priorities in AHP by interval regression analysis. European Journal of Operational Research, 158(3), 745-754. https://doi.org/10.1016/S0377-2217(03)00418-1
Sun, R. X., Hu, J. H., Zhou, J. D., & Chen, X. H. (2018). A hesitant fuzzy linguistic projection-based MABAC method for patients’ prioritization. International Journal of Fuzzy Systems, 20(7), 21442160. https://doi.org/10.1007/s40815-017-0345-7
Tosun, Ö., & Akyüz, G. (2015). A fuzzy TODIM approach for the supplier selection problem. International Journal of Computational Intelligence Systems, 8(2), 317-329. https://doi.org/10.1080/18756891.2015.1001954
Turskis, Z., & Zavadskas, E. K. (2010a). A new fuzzy additive ratio assessment method (ARAS-F). Case study: the analysis of fuzzy multiple criteria in order to select the logistic centers location. Transport, 25, 423-432. https://doi.org/10.3846/transport.2010.52
Turskis, Z., & Zavadskas, E. K. (2010b). A novel method for multiple criteria analysis: grey additive ratio assessment (ARAS-G) method. Informatica, 21, 597-610. https://iospress.metapress.com/index/N7J8531184645761
Tuysuz, F., & Berna, Ş. (2017). A hesitant fuzzy linguistic term sets-based AHP approach for analyzing the performance evaluation factors: an application to cargo sector. Complex & Intelligent Systems, 2, 1-9. https://doi.org/10.1007/s40747-017-0044-x
Xu, Z. S. (2013). Priority weight intervals derived from intuitionistic multiplicative preference relations. IEEE Transactions on Fuzzy Systems, 21, 642-654. https://doi.org/10.1109/TFUZZ.2012.2226893
Yu, C. X., Shao, Y. F., Wang, K., & Zhang, L. P. (2019). A group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environment. Expert Systems with Applications, 121, 1-17. https://doi.org/10.1016/j.eswa.2018.12.010
Zarbakhshnia, N., Soleimani, H., & Ghaderi, H. (2018). Sustainable third-party reverse logistics provider evaluation and selection using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria. Applied Soft Computing, 65, 307-319. https://doi.org/10.1016/j.asoc.2018.01.023
Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy, 16, 159-172. https://doi.org/10.3846/tede.2010.10
Zavadskas, E. K., Turskis, Z., & Vilutiene, T. (2010). Multiple criteria analysis of foundation instalment alternatives by applying additive ratio assessment (ARAS) method. Archives of Civil & Mechanical Engineering, 10, 123-141. https://doi.org/10.1016/S1644-9665(12)60141-1
Zhang, L., Hu, H. P., & Zhang, D. (2015). A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance. Financial Innovation, 1(1), 14. https://doi.org/10.1186/s40854-015-0014-5
Zhang, Z., & Wu, C. (2014). Hesitant fuzzy linguistic aggregation operators and their applications to multiple attribute group decision making. Journal of Intelligent & Fuzzy Systems, 26, 2185-2202. https://doi.org/10.3233/IFS-130893
Zhao, D., Wang, D., & Wang, B. (2018). Research on operational risk evaluation of online supply chain finance based on fuzzy AHP. Basic & Clinical Pharmacology & Toxicology, 122, 49-49.
Zheng, Y. H., Xu, Z. S, He, Y., & Liao, H. C. (2018). Severity assessment of chronic obstructive pulmonary disease based on hesitant fuzzy linguistic COPRAS method. Applied Soft Computing, 69, 60-71. https://doi.org/10.1016/j.asoc.2018.04.035
Zhu, B., & Xu, Z. S. (2014). Consistency measures for hesitant fuzzy linguistic preference relations. IEEE Transactions on Fuzzy Systems, 22, 35-45. https://doi.org/10.1109/TFUZZ.2013.2245136
Armin, C., Mohammad, M. P., & Mostafa, H. (2018). Applying a hybrid BWM-VIKOR approach to supplier selection: a case study in the Iranian agricultural implements industry. International Journal of Applied Decision Sciences, 11(3), 274-301. https://doi.org/10.1504/IJADS.2018.092796
Badi, I. A., Abdulshahed, A. M., & Shetwan, A. G. (2018). A case study of supplier selection for a steelmaking company in Libya by using the combinative distance-based assessment (CODAS) model. Decision Making: Applications in Management and Engineering, 1(1), 1-12. https://doi.org/10.31181/dmame180101b
Badi, I., & Ballem, M. (2018). Supplier selection using rough BWM-MAIRCA model: a case study in pharmaceutical supplying in Libya. Decision Making: Applications in Management and Engineering, 1(2), 16-33. https://doi.org/10.31181/dmame1802016b
Baležentis, T., & Streimikiene, D. (2017). Multi-criteria ranking of energy generation scenarios with Monte Carlo simulation. Applied Energy, 185, 862-871. https://doi.org/10.1016/j.apenergy.2016.10.085
Beg, I., & Rashid, T. (2013). TOPSIS for hesitant fuzzy linguistic term sets. International Journal of Intelligent Systems, 28, 1162-1171. https://doi.org/10.1002/int.21623
Büyüközkan, G., & Göcer, F. (2018). An extension of ARAS methodology under interval valued intuitionistic fuzzy environment for digital supply chain. Applied Soft Computing, 69, 634-654. https://doi.org/10.1016/j.asoc.2018.04.040
Dahooie, J. H., Zavadskas, E. K., Abolhasani, M., Vanaki, A., & Turskis, Z. (2018). A novel approach for evaluation of projects using an interval-valued fuzzy additive ratio assessment (ARAS) method: a case study of oil and gas well drilling projects. Symmetry, 10(2), 45. https://doi.org/10.3390/sym10020045
Ecer, F. (2018). An integrated fuzzy AHP and ARAS model to evaluate mobile banking services. Technological and Economic Development of Economy, 24(2), 1-26. https://doi.org/10.3846/20294913.2016.1255275
Ghadikolaei, A. S., Madhoushi, M., & Divsalar, M. (2018). Extension of the VIKOR method for group decision making with extended hesitant fuzzy linguistic information. Neural Computing and Applications, 30(12), 3589-3602. https://doi.org/10.1007/s00521-017-2944-5
Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31. https://doi.org/10.1016/j.knosys.2017.01.010
Hafezalkotob, A., Hafezalkotob, A., Liao, H. C., & Herrera, F. (2019a). An overview of MULTIMOORA for multi-criteria decision-making: Theory, developments, applications, and challenges. Information Fusion, 51, 145-177. https://doi.org/10.1016/j.inffus.2018.12.002
Hafezalkotob, A., Hafezalkotob, A., Liao, H. C., & Herrera, F (2019b). Interval MULTIMOORA method: Integrating interval Borda rule and interval best-west-method-based weighting model. IEEE Transactions on Cybernetics, 99, 1-13. https://doi.org/10.1109/TCYB.2018.2889730
Karabasevic, D., Zavadskas, E. K., Turskis, Z., & Stanujkic, D. (2016). The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties. Informatica, 27, 49-65. https://doi.org/10.15388/Informatica.2016.76
Keršulienė, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243-258. https://doi.org/10.3846/jbem.2010.12
Keshavarz Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., Hooshmand, R., & Antuchevičienė, J. (2017). Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. Journal of Business Economics and Management, 18(1), 1-19. https://doi.org/10.3846/16111699.2016.1278559
Liao, C. N., Fu, Y. K., & Wu, L. C. (2016). Integrated FAHP, ARAS-F and MSGP methods for green supplier evaluation and selection. Technological and Economic Development of Economy, 22, 651669. https://doi.org/10.3846/20294913.2015.1072750
Liao, H. C., Mi, X. M., Yu, Q., & Luo, L. (2019a). Hospital performance evaluation by a hesitant fuzzy linguistic best worst method with inconsistency repairing. Journal of Cleaner Production, 232, 657671. https://doi.org/10.1016/j.jclepro.2019.05.308
Liao, H. C., Qin, R., Gao, C. Y., Wu, X.L., Hafezalkotob, A., & Herrera, F. (2019b). Score-HeDLiSF: A score function of hesitant fuzzy linguistic term set based on hesitant degrees and linguistic scale functions: An application to unbalanced hesitant fuzzy linguistic MULTIMOORA, Information Fusion, 48, 39-54. https://doi.org/10.1016/j.inffus.2018.08.006
Liao, H. C., Xu, Z. S., Herrera-Viedma, E., & Herrera, F. (2018). Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the-art survey. International Journal of Fuzzy Systems, 20(7), 2084-2110. https://doi.org/10.1007/s40815-017-0432-9
Liao, H. C., Xu, Z. S., & Zeng, X. J. (2015a). Hesitant fuzzy linguistic VIKOR method and its application in qualitative multiple criteria decision making. IEEE Transactions on Fuzzy Systems, 23(5), 1343-1355. https://doi.org/10.1109/TFUZZ.2014.2360556
Liao, H. C., Xu, Z. S., Zeng, X. J., & Merigó, J. M. (2015b). Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowledge-Based Systems, 76, 127-138. https://doi.org/10.1016/j.knosys.2014.12.009
Mardani, A., Jusoh, A., Halicka, K., Ejdys, J., Magruk, A., & Ahmad, U. N. U. (2018). Determining the utility in management by using multi-criteria decision support tools: a review. Economic ResearchEkonomska Istraživanja, 31, 1666-1716. https://doi.org/10.1080/1331677X.2018.1488600
Mi, X. M., Tang, M., Liao, H. C., Shen, W. J., & Lev, B. (2019a). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega, 87, 205-225. https://doi.org/10.1016/j.omega.2019.01.009
Mi, X. M., Wu, X. L., Tang, M., Liao, H. C., Al-Barakati, A., Altalhi, A. H., & Herrera, F. (2019b). Hesitant fuzzy linguistic analytic hierarchical process with prioritization, consistency checking and inconsistency repairing. IEEE Access, 7(1), 44135-44149. https://doi.org/10.1109/ACCESS.2019.2908701
Mou, Q., Xu, Z. S., & Liao, H. C. (2016). An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences, 374, 224-239. https://doi.org/10.1016/j.ins.2016.08.074
Radović, D., Stević, Ž., Pamučar, D., Zavadskas, E. K., Badi, I., Antucheviciene, J., & Turskis, Z. (2018). Measuring performance in transportation companies in developing countries: a novel rough ARAS model. Symmetry, 10, 1-24. https://doi.org/10.3390/sym10100434
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
Rodríguez, R. M., Martinez, L., & Herrera, F. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1), 109-119. https://doi.org/10.1109/TFUZZ.2011.2170076
Shi, L. L., & Ye, J. (2017). Cosine measures of linguistic neutrosophic numbers and their application in multiple attribute group decision-making. Information, 8(4), 117. https://doi.org/10.3390/info8040117
Sremac, S., Stević, Ž., Pamučar, D., Arsić, M., & Matić, B. (2018). Evaluation of a third-party logistics (3PL) provider using a rough SWARA–WASPAS model based on a new rough dombi aggregator. Symmetry, 10(8), 305. https://doi.org/10.3390/sym10080305
Stanujkic, D., Zavadskas, E. K., Karabasevic, D., Turskis, Z., & Keršulienė, V. (2017). New group decision-making ARCAS approach based on the integration of the SWARA and the ARAS methods adapted for negotiations. Journal of Business Economics & Management, 18, 599-618. https://doi.org/10.3846/16111699.2017.1327455
Stević, Ž., Pamučar, D., Zavadskas, E. K., Ćirović, G., & Prentkovskis, O. (2017). The selection of wagons for the internal transport of a logistics company: A novel approach based on rough BWM and rough SAW methods. Symmetry, 9(11), 264. https://doi.org/10.3390/sym9110264
Sugihara, K., Ishii, H., & Tanaka, H. (2004). Interval priorities in AHP by interval regression analysis. European Journal of Operational Research, 158(3), 745-754. https://doi.org/10.1016/S0377-2217(03)00418-1
Sun, R. X., Hu, J. H., Zhou, J. D., & Chen, X. H. (2018). A hesitant fuzzy linguistic projection-based MABAC method for patients’ prioritization. International Journal of Fuzzy Systems, 20(7), 21442160. https://doi.org/10.1007/s40815-017-0345-7
Tosun, Ö., & Akyüz, G. (2015). A fuzzy TODIM approach for the supplier selection problem. International Journal of Computational Intelligence Systems, 8(2), 317-329. https://doi.org/10.1080/18756891.2015.1001954
Turskis, Z., & Zavadskas, E. K. (2010a). A new fuzzy additive ratio assessment method (ARAS-F). Case study: the analysis of fuzzy multiple criteria in order to select the logistic centers location. Transport, 25, 423-432. https://doi.org/10.3846/transport.2010.52
Turskis, Z., & Zavadskas, E. K. (2010b). A novel method for multiple criteria analysis: grey additive ratio assessment (ARAS-G) method. Informatica, 21, 597-610. https://iospress.metapress.com/index/N7J8531184645761
Tuysuz, F., & Berna, Ş. (2017). A hesitant fuzzy linguistic term sets-based AHP approach for analyzing the performance evaluation factors: an application to cargo sector. Complex & Intelligent Systems, 2, 1-9. https://doi.org/10.1007/s40747-017-0044-x
Xu, Z. S. (2013). Priority weight intervals derived from intuitionistic multiplicative preference relations. IEEE Transactions on Fuzzy Systems, 21, 642-654. https://doi.org/10.1109/TFUZZ.2012.2226893
Yu, C. X., Shao, Y. F., Wang, K., & Zhang, L. P. (2019). A group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environment. Expert Systems with Applications, 121, 1-17. https://doi.org/10.1016/j.eswa.2018.12.010
Zarbakhshnia, N., Soleimani, H., & Ghaderi, H. (2018). Sustainable third-party reverse logistics provider evaluation and selection using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria. Applied Soft Computing, 65, 307-319. https://doi.org/10.1016/j.asoc.2018.01.023
Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy, 16, 159-172. https://doi.org/10.3846/tede.2010.10
Zavadskas, E. K., Turskis, Z., & Vilutiene, T. (2010). Multiple criteria analysis of foundation instalment alternatives by applying additive ratio assessment (ARAS) method. Archives of Civil & Mechanical Engineering, 10, 123-141. https://doi.org/10.1016/S1644-9665(12)60141-1
Zhang, L., Hu, H. P., & Zhang, D. (2015). A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance. Financial Innovation, 1(1), 14. https://doi.org/10.1186/s40854-015-0014-5
Zhang, Z., & Wu, C. (2014). Hesitant fuzzy linguistic aggregation operators and their applications to multiple attribute group decision making. Journal of Intelligent & Fuzzy Systems, 26, 2185-2202. https://doi.org/10.3233/IFS-130893
Zhao, D., Wang, D., & Wang, B. (2018). Research on operational risk evaluation of online supply chain finance based on fuzzy AHP. Basic & Clinical Pharmacology & Toxicology, 122, 49-49.
Zheng, Y. H., Xu, Z. S, He, Y., & Liao, H. C. (2018). Severity assessment of chronic obstructive pulmonary disease based on hesitant fuzzy linguistic COPRAS method. Applied Soft Computing, 69, 60-71. https://doi.org/10.1016/j.asoc.2018.04.035
Zhu, B., & Xu, Z. S. (2014). Consistency measures for hesitant fuzzy linguistic preference relations. IEEE Transactions on Fuzzy Systems, 22, 35-45. https://doi.org/10.1109/TFUZZ.2013.2245136