925
Views
6
CrossRef citations to date
0
Altmetric
Articles

Multi-criteria group decision making with a partial-ranking-based ordinal consensus reaching process for automotive development management

ORCID Icon & ORCID Icon
Pages 4839-4864 | Received 01 Apr 2021, Accepted 12 Dec 2021, Published online: 29 Dec 2021

References

  • Bouyssou, D. (1996). Outranking relations: Do they have special properties? Journal of Multi-Criteria Decision Analysis, 5(2), 99–111. https://doi.org/10.1002/(SICI)1099-1360(199606)5:2<99::AID-MCDA97>3.0.CO;2-8
  • Cook, W. D. (2006). Distance-based and ad hoc consensus models in ordinal preference ranking. European Journal of Operational Research, 172(2), 369–385. https://doi.org/10.1016/j.ejor.2005.03.048
  • Cook, W. D., Kress, M., & Seiford, L. (1986). Information and preference in partial orders: A bimatrix representation. Psychometrika, 51(2), 197–207. https://doi.org/10.1007/BF02293980
  • Cook, W. D., & Seiford, L. (1978). Priority ranking and consensus formation. Management Science, 24(16), 1721–1732. https://doi.org/10.1287/mnsc.24.16.1721
  • Di Bella, E., Gandullia, L., Leporatti, L., Montefiori, M., & Orcamo, P. (2018). Ranking and prioritization of emergency departments based on multi-indicator systems. Social Indicators Research, 136(3), 1089–1107. https://doi.org/10.1007/s11205-016-1537-5
  • Gou, X. J., Xu, Z. S., Zhou, W., & Herrera-Viedma, E. (2021). The risk assessment of construction project investment based on prospect theory with linguistic preference orderings. Economic Research-Ekonomska Istraživanja, 34(1), 709–731. https://doi.org/10.1080/1331677X.2020.1868324
  • Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2021). The ordinal input for cardinal output approach of non-compensatory composite indicators: The PROMETHEE scoring method. European Journal of Operational Research, 288(1), 225–246. https://doi.org/10.1016/j.ejor.2020.05.036
  • Herrera-Viedma, E., Cabrerizo, F. J., Kacprzyk, J., & Pedrycz, W. (2014). A review of soft consensus models in a fuzzy environment. Information Fusion, 17(1), 4–13. https://doi.org/10.1016/j.inffus.2013.04.002
  • Herrera-Viedma, E., Herrera, F., & Chiclana, F. (2002). A consensus model for multiperson decision making with different preference structures. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 32(3), 394–402. https://doi.org/10.1109/TSMCA.2002.802821
  • Jabeur, K., & Martel, J. (2010). An agreement index with respect to a consensus preorder. Group Decision and Negotiation, 19(6), 571–590. https://doi.org/10.1007/s10726-009-9160-3
  • Jabeur, K., Martel, J., & Khélifa, S. (2004). A distance-based collective preorder integrating the relative importance of the group's members. Group Decision and Negotiation, 13(4), 327–349. https://doi.org/10.1023/B:GRUP.0000042894.00775.75
  • Jiang, K., Luo, Z. Q., Feng, Z. X., Huang, Z. P., & Yu, Z. H. (2018). Subjective evaluation of automobile power performance based on RA-AHP. Cognition, Technology & Work, 20(3), 413–424. https://doi.org/10.1007/s10111-018-0488-9
  • Kacprzyk, J., & Fedrizzi, M. (1988). A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences. European Journal of Operational Research, 34(3), 316–325. https://doi.org/10.1016/0377-2217(88)90152-X
  • Karimi Eskandary, P., Khajepour, A., Wong, A., & Ansari, M. (2016). Analysis and optimization of air suspension system with independent height and stiffness tuning. International Journal of Automotive Technology, 17(5), 807–816. https://doi.org/10.1007/s12239-016-0079-9
  • Labella, Á., Ishizaka, A., & Martínez, L. (2021). Consensual group-AHPSort: Applying consensus to GAHPSort in sustainable development and industrial engineering. Computers & Industrial Engineering, 152, 107013. https://doi.org/10.1016/j.cie.2020.107013
  • Labella, Á., Liu, H. B., Rodríguez, R. M., & Martínez, L. (2020). A cost consensus metric for consensus reaching processes based on a comprehensive minimum cost model. European Journal of Operational Research, 281(2), 316–331. https://doi.org/10.1016/j.ejor.2019.08.030
  • Leik, R. K. (1966). A measure of ordinal consensus. The Pacific Sociological Review, 9(2), 85–90. https://doi.org/10.2307/1388242
  • Liao, H. C., Li, Z. M., Zeng, X. J., & Liu, W. S. (2017). A comparison of distinct consensus measures for group decision making with intuitionistic fuzzy preference relations. International Journal of Computational Intelligence Systems, 10(1), 456–469.
  • Liao, H. C., Wu, X. L., Liang, X. D., Xu, J. P., & Herrera, F. (2018a). A new hesitant fuzzy linguistic ORESTE method for hybrid multi-criteria decision making. IEEE Transactions on Fuzzy Systems, 26(6), 3793–3807. https://doi.org/10.1109/TFUZZ.2018.2849368
  • Liao, H. C., Wu, X. L., Liang, X. D., Yang, J. B., Xu, D. L., & Herrera, F. (2018b). A continuous interval-valued linguistic ORESTE method for multi-criteria group decision making. Knowledge-Based Systems, 153, 65–77. https://doi.org/10.1016/j.knosys.2018.04.022
  • Liao, H. C., Xue, J. F., Nilashi, M., Wu, X. L., & Antucheviciene, J. (2020). Partner selection for automobile manufacturing enterprises with a Q-rung orthopair fuzzy double normalizaion-based multi-aggregation method1. Transformations in Business and Economics, 19(2), 338–368.
  • Liao, H. C., Xu, Z. S., 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. 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 Research-Ekonomska Istraživanja, 31(1), 1666–1716. https://doi.org/10.1080/1331677X.2018.1488600
  • Meng, F. Y., & Ding, Y. Q. (2020). An improved ELECTRE-III for recommending new energy vehicles with heterogeneous decision-making information. International Journal of Modelling, Identification and Control, 34(4), 328–341. https://doi.org/10.1504/IJMIC.2020.112298
  • Morente-Molinera, J. A., Wu, X., Morfeq, A., Al-Hmouz, R., & Herrera-Viedma, E. (2020). A novel multi-criteria group decision-making method for heterogeneous and dynamic contexts using multi-granular fuzzy linguistic modelling and consensus measures. Information Fusion, 53, 240–250. https://doi.org/10.1016/j.inffus.2019.06.028
  • Mousset, C. (2009). Families of relations modelling preferences under incomplete information. European Journal of Operational Research, 192(2), 538–548. https://doi.org/10.1016/j.ejor.2007.09.030
  • Pastijn, H., & Leysen, J. (1989). Constructing an outranking relation with ORESTE. Mathematical and Computer Modelling, 12(10-11), 1255–1268. https://doi.org/10.1016/0895-7177(89)90367-1
  • Peng, H. G., Wang, J. Q., & Zhang, H. Y. (2020). Multi-criteria outranking method based on probability distribution with probabilistic linguistic information. Computers & Industrial Engineering, 141, https://doi.org/10.1016/j.cie.2020.106318
  • 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. https://doi.org/10.1109/TFUZZ.2011.2170076
  • Roubens, M. (1982). Preference relations on actions and criteria in multicriteria decision making. European Journal of Operational Research, 10(1), 51–55. https://doi.org/10.1016/0377-2217(82)90131-X
  • Roubens, M., & Vincke, P. (1985). Preference Modelling. In Lecture Notes in Economics and Mathematical Systems (250). Springer-Verlag.
  • See, T. K., & Lewis, K. (2006). A formal approach to handling conflicts in multiattribute group decision making. Journal of Mechanical Design, 128(4), 678–688. https://doi.org/10.1115/1.2197836
  • Sellak, H., Ouhbi, B., Frikh, B., & Ikken, B. (2019). Expertise-based consensus building for MCGDM with hesitant fuzzy linguistic information. Information Fusion, 50, 54–70. https://doi.org/10.1016/j.inffus.2018.10.003
  • Shen, F., Liang, C., & Yang, Z. Y. (2021). Combined probabilistic linguistic term set and ELECTRE II method for solving a venture capital project evaluation problem. Economic Research-Ekonomska Istraživanja, 1–23. https://doi.org/10.1080/1331677X.2021.1880957
  • Tang, M., & Liao, H. C. (2021). From conventional group decision making to large-scale group decision making: what are the challenges and how to meet them in big data era? A state-of-the-art survey. Omega, 100, https://doi.org/10.1016/j.omega.2019.102141
  • Tang, M., Zhou, X. Y., Liao, H. C., Xu, J. P., Fujita, H., & Herrera, F. (2019). Ordinal consensus measure with objective threshold for heterogeneous large-scale group decision making. Knowledge-Based Systems, 180, 62–74. https://doi.org/10.1016/j.knosys.2019.05.019
  • Tian, Z. P., Nie, R. X., Wang, J. Q., & Zhang, H. Y. (2019). Signed distance-based ORESTE for multicriteria group decision-making with multigranular unbalanced hesitant fuzzy linguistic information. Expert Systems, 36(1), e12350–24. https://doi.org/10.1111/exsy.12350
  • Wu, X. L., & Liao, H. C. (2019). A consensus-based probabilistic linguistic gained and lost dominance score method. European Journal of Operational Research, 272(3), 1017–1027. https://doi.org/10.1016/j.ejor.2018.07.044
  • Wu, Z. B., & Xu, J. P. (2018). A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters. Information Fusion, 41, 217–231. https://doi.org/10.1016/j.inffus.2017.09.011
  • Xu, Z. S., & Wang, H. (2017). On the syntax and semantics of virtual linguistic terms for information fusion in decision making. Information Fusion, 34, 43–48. https://doi.org/10.1016/j.inffus.2016.06.002
  • Yildizbaşi, A., Çalik, A., Paksoy, T., Zanjirani Farahani, R., & Weber, G. (2018). Multi-level optimization of an automotive closed-loop supply chain network with interactive fuzzy programming approaches. Technological and Economic Development of Economy, 24(3), 1004–1028. https://doi.org/10.3846/20294913.2016.1253044
  • Yoo, Y., Escobedo, A. R., & Skolfield, J. K. (2020). A new correlation coefficient for comparing and aggregating non-strict and incomplete rankings. European Journal of Operational Research, 285(3), 1025–1041. https://doi.org/10.1016/j.ejor.2020.02.027