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A Choquet integral-based hesitant fuzzy gained and lost dominance score method for multi-criteria group decision making considering the risk preferences of experts: Case study of higher business education evaluation

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A Choquet integral-based hesitant fuzzy gained and lost dominance score method for multi-criteria group decision making considering the risk preferences of experts: Case study of higher business education evaluation

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dc.contributor.author Liao, Zhiqiang es_ES
dc.contributor.author Liao, Huchang es_ES
dc.contributor.author Tang, Ming es_ES
dc.contributor.author Al-Barakati, Abdullah es_ES
dc.contributor.author Llopis-Albert, Carlos es_ES
dc.date.accessioned 2021-07-17T03:34:40Z
dc.date.available 2021-07-17T03:34:40Z
dc.date.issued 2020-10 es_ES
dc.identifier.issn 1566-2535 es_ES
dc.identifier.uri http://hdl.handle.net/10251/169417
dc.description.abstract [EN] With the rapid development of higher business education, higher business education evaluation has attracted considerable attention of researchers and practitioners. The higher business education evaluation is an essential part of the development of a business school, which has a direct impact on its resource distribution. The higher business education evaluation can be considered as a multiple criteria group decision making (MCGDM) problem that involves a group of experts. Due to the complexity of the decision-making problem, decision criteria are not fully independent to each other, and the assumption of complete rationality of experts is usually invalid in many situations. In this paper, we propose a Choquet integral-based hesitant fuzzy gained and lost dominance score method to address the two important issues regarding the interactions among criteria and the behavior preference characteristics of experts in MCGDM problems. Firstly, a comprehensive distance measure of hesitant fuzzy sets is introduced by considering the relative importance of two separations. Then, a Choquet integral-based hesitant fuzzy gained and lost dominance score method based on the prospect theory is proposed to address the MCGDM problems in which experts make decision with the risk preference psychology. Finally, an illustrative example of higher business education evaluation is provided to demonstrate the applicability of the proposed method, and the sensitivity and comparative analysis are also completed to verify the validity of the proposed method. es_ES
dc.description.sponsorship The work was supported by the National Natural Science Foundation of China (71771156) and the 2019 Soft Science Project of Sichuan Science and Technology Department (No. 2019JDR0141). es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Information Fusion es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Multiple criteria group decision making es_ES
dc.subject Hesitant fuzzy set es_ES
dc.subject Gained and lost dominance score method es_ES
dc.subject Choquet integral es_ES
dc.subject Prospect theory es_ES
dc.subject.classification INGENIERIA MECANICA es_ES
dc.title A Choquet integral-based hesitant fuzzy gained and lost dominance score method for multi-criteria group decision making considering the risk preferences of experts: Case study of higher business education evaluation es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.inffus.2020.05.003 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/SPDST//2019JDR0141/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//71771156/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Mecánica y de Materiales - Departament d'Enginyeria Mecànica i de Materials es_ES
dc.description.bibliographicCitation Liao, Z.; Liao, H.; Tang, M.; Al-Barakati, A.; Llopis-Albert, C. (2020). A Choquet integral-based hesitant fuzzy gained and lost dominance score method for multi-criteria group decision making considering the risk preferences of experts: Case study of higher business education evaluation. Information Fusion. 62:121-133. https://doi.org/10.1016/j.inffus.2020.05.003 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.inffus.2020.05.003 es_ES
dc.description.upvformatpinicio 121 es_ES
dc.description.upvformatpfin 133 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 62 es_ES
dc.relation.pasarela S\413911 es_ES
dc.contributor.funder National Natural Science Foundation of China es_ES
dc.contributor.funder Department of Science and Technology of Sichuan Province es_ES
dc.description.references Aggarwal, M., & Fallah Tehrani, A. (2019). Modelling Human Decision Behaviour with Preference Learning. INFORMS Journal on Computing, 31(2), 318-334. doi:10.1287/ijoc.2018.0823 es_ES
dc.description.references Angilella, S., Corrente, S., Greco, S., & Słowiński, R. (2016). Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis in multiple criteria hierarchy process for the Choquet integral preference model. Omega, 63, 154-169. doi:10.1016/j.omega.2015.10.010 es_ES
dc.description.references Bedregal, B., Reiser, R., Bustince, H., Lopez-Molina, C., & Torra, V. (2014). Aggregation functions for typical hesitant fuzzy elements and the action of automorphisms. Information Sciences, 255, 82-99. doi:10.1016/j.ins.2013.08.024 es_ES
dc.description.references Bottero, M., Ferretti, V., Figueira, J. R., Greco, S., & Roy, B. (2018). On the Choquet multiple criteria preference aggregation model: Theoretical and practical insights from a real-world application. European Journal of Operational Research, 271(1), 120-140. doi:10.1016/j.ejor.2018.04.022 es_ES
dc.description.references Bustince, H., Barrenechea, E., Pagola, M., Fernandez, J., Xu, Z., Bedregal, B., … De Baets, B. (2016). A Historical Account of Types of Fuzzy Sets and Their Relationships. IEEE Transactions on Fuzzy Systems, 24(1), 179-194. doi:10.1109/tfuzz.2015.2451692 es_ES
dc.description.references Bustince, H., Fernandez, J., Kolesárová, A., & Mesiar, R. (2013). Generation of linear orders for intervals by means of aggregation functions. Fuzzy Sets and Systems, 220, 69-77. doi:10.1016/j.fss.2012.07.015 es_ES
dc.description.references Chen, Z.-S., Chin, K.-S., Li, Y.-L., & Yang, Y. (2016). Proportional hesitant fuzzy linguistic term set for multiple criteria group decision making. Information Sciences, 357, 61-87. doi:10.1016/j.ins.2016.04.006 es_ES
dc.description.references CHIOU, H., TZENG, G., & CHENG, D. (2005). Evaluating sustainable fishing development strategies using fuzzy MCDM approach. Omega, 33(3), 223-234. doi:10.1016/j.omega.2004.04.011 es_ES
dc.description.references Choquet, G. (1954). Theory of capacities. Annales de l’institut Fourier, 5, 131-295. doi:10.5802/aif.53 es_ES
dc.description.references Corrente, S., Figueira, J. R., Greco, S., & Słowiński, R. (2017). A robust ranking method extending ELECTRE III to hierarchy of interacting criteria, imprecise weights and stochastic analysis. Omega, 73, 1-17. doi:10.1016/j.omega.2016.11.008 es_ES
dc.description.references Fu, Z., Wu, X., Liao, H., & Herrera, F. (2018). Underground Mining Method Selection With the Hesitant Fuzzy Linguistic Gained and Lost Dominance Score Method. IEEE Access, 6, 66442-66458. doi:10.1109/access.2018.2878784 es_ES
dc.description.references Grabisch, M. (1996). The application of fuzzy integrals in multicriteria decision making. European Journal of Operational Research, 89(3), 445-456. doi:10.1016/0377-2217(95)00176-x es_ES
dc.description.references Grabisch, M. (1997). k-order additive discrete fuzzy measures and their representation. Fuzzy Sets and Systems, 92(2), 167-189. doi:10.1016/s0165-0114(97)00168-1 es_ES
dc.description.references Jin, L., Kalina, M., Mesiar, R., & Borkotokey, S. (2018). Discrete Choquet Integrals for Riemann Integrable Inputs With Some Applications. IEEE Transactions on Fuzzy Systems, 26(5), 3164-3169. doi:10.1109/tfuzz.2018.2792458 es_ES
dc.description.references Joshi, D., & Kumar, S. (2016). Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multi-criteria group decision making. European Journal of Operational Research, 248(1), 183-191. doi:10.1016/j.ejor.2015.06.047 es_ES
dc.description.references Kabak, M., & Dağdeviren, M. (2014). A HYBRID MCDM APPROACH TO ASSESS THE SUSTAINABILITY OF STUDENTS’ PREFERENCES FOR UNIVERSITY SELECTION. Technological and Economic Development of Economy, 20(3), 391-418. doi:10.3846/20294913.2014.883340 es_ES
dc.description.references Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263. doi:10.2307/1914185 es_ES
dc.description.references Kao, C., & Lin, P.-H. (2011). Qualitative factors in data envelopment analysis: A fuzzy number approach. European Journal of Operational Research, 211(3), 586-593. doi:10.1016/j.ejor.2010.12.004 es_ES
dc.description.references Kobina, A., Liang, D., & He, X. (2017). Probabilistic Linguistic Power Aggregation Operators for Multi-Criteria Group Decision Making. Symmetry, 9(12), 320. doi:10.3390/sym9120320 es_ES
dc.description.references Kuo, T. (2017). A modified TOPSIS with a different ranking index. European Journal of Operational Research, 260(1), 152-160. doi:10.1016/j.ejor.2016.11.052 es_ES
dc.description.references Liao, H., Tang, M., Zhang, X., & Al-Barakati, A. (2019). Detecting and Visualizing in the Field of Hesitant Fuzzy Sets: A Bibliometric Analysis from 2009 to 2018. International Journal of Fuzzy Systems, 21(5), 1289-1305. doi:10.1007/s40815-019-00656-4 es_ES
dc.description.references Liao, H., Xu, Z., Herrera-Viedma, E., & Herrera, F. (2017). 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. doi:10.1007/s40815-017-0432-9 es_ES
dc.description.references LIAO, H., XU, Z., & XIA, M. (2014). MULTIPLICATIVE CONSISTENCY OF HESITANT FUZZY PREFERENCE RELATION AND ITS APPLICATION IN GROUP DECISION MAKING. International Journal of Information Technology & Decision Making, 13(01), 47-76. doi:10.1142/s0219622014500035 es_ES
dc.description.references Liao, H., Xu, Z., Zeng, X.-J., & Merigó, J. M. (2015). Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowledge-Based Systems, 76, 127-138. doi:10.1016/j.knosys.2014.12.009 es_ES
dc.description.references Liao, H., Yu, J., Wu, X., Al-Barakati, A., Altalhi, A., & Herrera, F. (2019). Life satisfaction evaluation in earthquake-hit area by the probabilistic linguistic GLDS method integrated with the logarithm-multiplicative analytic hierarchy process. International Journal of Disaster Risk Reduction, 38, 101190. doi:10.1016/j.ijdrr.2019.101190 es_ES
dc.description.references Liu, H., Song, Y., & Yang, G. (2019). Cross-efficiency evaluation in data envelopment analysis based on prospect theory. European Journal of Operational Research, 273(1), 364-375. doi:10.1016/j.ejor.2018.07.046 es_ES
dc.description.references Pang, Q., Wang, H., & Xu, Z. (2016). Probabilistic linguistic term sets in multi-attribute group decision making. Information Sciences, 369, 128-143. doi:10.1016/j.ins.2016.06.021 es_ES
dc.description.references Peng, D.-H., Gao, C.-Y., & Gao, Z.-F. (2013). Generalized hesitant fuzzy synergetic weighted distance measures and their application to multiple criteria decision-making. Applied Mathematical Modelling, 37(8), 5837-5850. doi:10.1016/j.apm.2012.11.016 es_ES
dc.description.references Qin, J., Liu, X., & Pedrycz, W. (2017). An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. European Journal of Operational Research, 258(2), 626-638. doi:10.1016/j.ejor.2016.09.059 es_ES
dc.description.references Rodríguez, R. M., Bedregal, B., Bustince, H., Dong, Y. C., Farhadinia, B., Kahraman, C., … Herrera, F. (2016). A position and perspective analysis of hesitant fuzzy sets on information fusion in decision making. Towards high quality progress. Information Fusion, 29, 89-97. doi:10.1016/j.inffus.2015.11.004 es_ES
dc.description.references Rodriguez, R. M., Martinez, L., & Herrera, F. (2012). Hesitant Fuzzy Linguistic Term Sets for Decision Making. IEEE Transactions on Fuzzy Systems, 20(1), 109-119. doi:10.1109/tfuzz.2011.2170076 es_ES
dc.description.references Rodríguez, R. M., Martínez, L., Torra, V., Xu, Z. S., & Herrera, F. (2014). Hesitant Fuzzy Sets: State of the Art and Future Directions. International Journal of Intelligent Systems, 29(6), 495-524. doi:10.1002/int.21654 es_ES
dc.description.references Tang, X., Peng, Z., Ding, H., Cheng, M., & Yang, S. (2018). Novel distance and similarity measures for hesitant fuzzy sets and their applications to multiple attribute decision making. Journal of Intelligent & Fuzzy Systems, 34(6), 3903-3916. doi:10.3233/jifs-169561 es_ES
dc.description.references Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323. doi:10.1007/bf00122574 es_ES
dc.description.references Wang, W., Liu, X., Qin, Y., & Fu, Y. (2018). A risk evaluation and prioritization method for FMEA with prospect theory and Choquet integral. Safety Science, 110, 152-163. doi:10.1016/j.ssci.2018.08.009 es_ES
dc.description.references Wang, H., & Xu, Z. (2016). Admissible orders of typical hesitant fuzzy elements and their application in ordered information fusion in multi-criteria decision making. Information Fusion, 29, 98-104. doi:10.1016/j.inffus.2015.08.009 es_ES
dc.description.references Wei, G., Zhao, X., & Lin, R. (2013). Some hesitant interval-valued fuzzy aggregation operators and their applications to multiple attribute decision making. Knowledge-Based Systems, 46, 43-53. doi:10.1016/j.knosys.2013.03.004 es_ES
dc.description.references Xia, M., & Xu, Z. (2011). Hesitant fuzzy information aggregation in decision making. International Journal of Approximate Reasoning, 52(3), 395-407. doi:10.1016/j.ijar.2010.09.002 es_ES
dc.description.references Xu, Z., & Xia, M. (2011). Distance and similarity measures for hesitant fuzzy sets. Information Sciences, 181(11), 2128-2138. doi:10.1016/j.ins.2011.01.028 es_ES
dc.description.references Xu, Z., & Zhang, X. (2013). Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information. Knowledge-Based Systems, 52, 53-64. doi:10.1016/j.knosys.2013.05.011 es_ES
dc.description.references Yager, R. R. (2018). Multi-Criteria Decision Making with Interval Criteria Satisfactions Using the Golden Rule Representative Value. IEEE Transactions on Fuzzy Systems, 26(2), 1023-1031. doi:10.1109/tfuzz.2017.2709275 es_ES
dc.description.references Yu, P. L. (1973). A Class of Solutions for Group Decision Problems. Management Science, 19(8), 936-946. doi:10.1287/mnsc.19.8.936 es_ES


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