×

Likelihoods of interval type-2 trapezoidal fuzzy preference relations and their application to multiple criteria decision analysis. (English) Zbl 1360.91065

Summary: Interval type-2 fuzzy sets are useful and valuable for depicting uncertainty and managing imprecision in decision information. In particular, interval type-2 trapezoidal fuzzy numbers, as a special case of interval type-2 fuzzy sets, can efficiently express qualitative evaluations or assessments. In this work, the concept of the likelihoods of interval type-2 trapezoidal fuzzy preference relations based on lower and upper likelihoods is investigated, and the relevant properties are discussed. This paper focuses on the use of likelihoods in addressing multiple criteria decision analysis problems in which the evaluative ratings of the alternatives and the importance weights of the criteria are expressed as interval type-2 trapezoidal fuzzy numbers. A new likelihood-based decision-making method is developed using the useful concepts of likelihood-based performance indices, likelihood-based comprehensive evaluation values, and signed distance-based evaluation values. A simplified version of the proposed method is also provided to adapt the decision-making context in which the importance weights of the criteria take the form of ordinary numbers. The practical effectiveness of the proposed method is validated with four applications, and several comparative analyses are conducted to verify the advantages of the proposed method over other multiple criteria decision-making methods.

MSC:

91B06 Decision theory
Full Text: DOI

References:

[1] Abdullah, L.; Najib, L., A new type-2 fuzzy set of linguistic variables for the fuzzy analytic hierarchy process, Exp. Syst. Appl., 41, 7, 3297-3305 (2014)
[4] Ashtiani, B.; Haghighirad, F.; Makui, A.; Montazer, G., Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets, Appl. Soft Comput., 9, 2, 457-461 (2009)
[5] Baležentis, T.; Zeng, S., Group multi-criteria decision making based upon interval-valued fuzzy numbers: an extension of the MULTIMOORA method, Exp. Syst. Appl., 40, 2, 543-550 (2013)
[6] Castillo, O.; Melin, P., Optimization of type-2 fuzzy systems based on bio-inspired methods: a concise review, Inform. Sci., 205, 1-19 (2012)
[7] Castillo, O.; Melin, P.; Pedrycz, W., Design of interval type-2 fuzzy models through optimal granularity allocation, Appl. Soft Comput., 11, 8, 5590-5601 (2011)
[8] Celik, E.; Bilisik, O. N.; Erdogan, M.; Gumus, A. T.; Baracli, H., An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul, Transport. Res. Part E: Logist. Transport. Rev., 58, 28-51 (2013)
[9] Chen, T.-Y., An integrated approach for assessing criterion importance with interval type-2 fuzzy sets and signed distances, J. Chin. Inst. Indust. Eng., 28, 8, 553-572 (2011)
[10] Chen, T.-Y., Multiple criteria group decision-making with generalized interval-valued fuzzy numbers based on signed distances and incomplete weights, Appl. Math. Model., 36, 7, 3029-3052 (2012) · Zbl 1252.91047
[11] Chen, T.-Y., An interactive method for multiple criteria group decision analysis based on interval type-2 fuzzy sets and its application to medical decision making, Fuzzy Optimiz. Dec. Mak., 12, 3, 323-356 (2013) · Zbl 1429.91098
[12] Chen, T.-Y., A signed-distance-based approach to importance assessment and multi-criteria group decision analysis based on interval type-2 fuzzy set, Knowl. Inform. Syst., 35, 1, 193-231 (2013)
[13] Chen, T.-Y., A linear assignment method for multiple-criteria decision analysis with interval type-2 fuzzy sets, Appl. Soft Comput., 13, 5, 2735-2748 (2013)
[14] Chen, T.-Y., An ELECTRE-based outranking method for multiple criteria group decision making using interval type-2 fuzzy sets, Inform. Sci., 263, 1-21 (2014) · Zbl 1328.91046
[15] Chen, T.-Y., A PROMETHEE-based outranking method for multiple criteria decision analysis with interval type-2 fuzzy sets, Soft Comput., 18, 5, 923-940 (2014) · Zbl 1406.90057
[16] Chen, T.-Y.; Chang, C.-H.; Lu, J.-f. R., The extended QUALIFLEX method for multiple criteria decision analysis based on interval type-2 fuzzy sets and applications to medical decision making, Euro. J. Operat. Res., 226, 3, 615-625 (2013) · Zbl 1292.90185
[17] Chen, Y.-S.; Cheng, C.-H., Forecasting PGR of the financial industry using a rough sets classifier based on attribute-granularity, Knowl. Inform. Syst., 25, 1, 57-79 (2010)
[18] Chen, S.-M.; Lee, L.-W., Fuzzy multiple criteria hierarchical group decision-making based on interval type-2 fuzzy sets, IEEE Trans. Syst., Man, Cybernet.-Part A: Syst. Hum., 40, 5, 1120-1128 (2010)
[19] Chen, S.-M.; Lee, L.-W., Fuzzy multiple attributes group decision-making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets, Exp. Syst. Appl., 37, 1, 824-833 (2010)
[20] Chen, S.-M.; Lee, L.-W., Fuzzy decision-making based on likelihood-based comparison relations, IEEE Trans. Fuzzy Syst., 18, 3, 613-628 (2010)
[21] Chen, T.-Y.; Tsui, C.-W., Intuitionistic fuzzy QUALIFLEX method for optimistic and pessimistic decision making, Advan. Inform. Sci. Serv. Sci., 4, 14, 219-226 (2012)
[22] Chen, S.-M.; Wang, C.-Y., Fuzzy decision making systems based on interval type-2 fuzzy sets, Inform. Sci., 242, 1-21 (2013) · Zbl 1321.91024
[23] Greenfield, S.; Chiclana, F.; Coupland, S.; John, R., The collapsing method of defuzzification for discretised interval type-2 fuzzy sets, Inform. Sci., 179, 13, 2055-2069 (2009) · Zbl 1178.68586
[24] Han, Z.; Liu, P., A fuzzy multi-attribute decision-making method under risk with unknown attribute weights, Technol. Econ. Develop. Econ., 17, 2, 246-258 (2011)
[25] Hidalgo, D.; Castillo, O.; Melin, P., Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms, Inform. Sci., 179, 13, 2123-2145 (2009)
[26] Hidalgo, D.; Melin, P.; Castillo, O., An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms, Exp. Syst. Appl., 39, 4, 4590-4598 (2012)
[27] Lai, H.-L.; Chen, T.-Y., Client acceptance method for audit firms based on interval-valued fuzzy numbers, Technol. Econ. Develop. Econ. (2014)
[28] Leal-Ramírez, C.; Castillo, O.; Melin, P.; Rodríguez-Díaz, A., Simulation of the bird age-structured population growth based on an interval type-2 fuzzy cellular structure, Inform. Sci., 181, 3, 519-535 (2011)
[30] Li, D.-F., Linear programming method for MADM with interval-valued intuitionistic fuzzy sets, Exp. Syst. Appl., 37, 8, 5939-5945 (2010)
[31] Li, D.-F., TOPSIS-based nonlinear-programming methodology for multiattribute decision making with interval-valued intuitionistic fuzzy sets, IEEE Trans. Fuzzy Syst., 18, 2, 299-311 (2010)
[32] Li, D.-F., Closeness coefficient based nonlinear programming method for interval-valued intuitionistic fuzzy multiattribute decision making with incomplete preference information, Appl. Soft Comput., 11, 4, 3402-3418 (2011)
[33] Liou, J. J.H.; Tzeng, G. H., Comments on “multiple criteria decision making (MCDM) methods in economics: an overview”, Technol. Econ. Develop. Econ., 18, 4, 672-695 (2012)
[34] Liu, P.; Jin, F., A multi-attribute group decision-making method based on weighted geometric aggregation operators of interval-valued trapezoidal fuzzy numbers, Appl. Math. Model., 36, 6, 2498-2509 (2012) · Zbl 1246.91034
[35] Liu, P.; Su, Y., Multiple attribute decision making method based on the trapezoid fuzzy linguistic hybrid harmonic averaging operator, Informatica, 36, 1, 83-90 (2012) · Zbl 1260.68392
[36] Melin, P.; Castillo, O., A review on the applications of type-2 fuzzy logic in classification and pattern recognition, Exp. Syst. Appl., 40, 13, 5413-5423 (2013)
[37] Melin, P.; Olivas, F.; Castillo, O.; Valdez, F.; Soria, J.; Valdez, M., Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic, Exp. Syst. Appl., 40, 8, 3196-3206 (2013)
[38] Mendel, J. M., Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions (2001), Prentice-Hall: Prentice-Hall Upper-Saddle River, NJ · Zbl 0978.03019
[39] Ngan, S.-C., A type-2 linguistic set theory and its application to multi-criteria decision making, Comp. Indust. Eng., 64, 2, 721-730 (2013)
[40] Pedrycz, W., Granular Computing: Analysis and Design of Intelligent Systems (2013), CRC Press/Francis Taylor: CRC Press/Francis Taylor Boca Raton
[41] Razavi Hajiagha, S. H.; Hashemi, S. S.; Zavadskas, E. K., A complex proportional assessment method for group decision making in an interval-valued intuitionistic fuzzy environment, Technol. Econ. Develop. Econ., 19, 1, 22-37 (2013)
[42] Takáč, Z., Inclusion and subsethood measure for interval-valued fuzzy sets and for continuous type-2 fuzzy sets, Fuzzy Sets Syst., 224, 106-120 (2013) · Zbl 1284.03253
[43] Wang, J.-C.; Chen, T.-Y., A closeness coefficient-based multiple criteria decision-making method using interval type-2 fuzzy sets and its application to watershed site selection, J. Indust. Product. Eng., 31, 1, 1-16 (2014)
[44] Xu, Z. S.; Da, Q. L., A likelihood-based method for priorities of interval judgment matrices, Chin. J. Manage. Sci., 11, 1, 63-65 (2003)
[45] Zadeh, L. A., The concept of a linguistic variable and its application to approximate reasoning-I, Inform. Sci., 8, 3, 199-249 (1975) · Zbl 0397.68071
[46] Zavadskas, E. K.; Turskis, Z., Multiple criteria decision making (MCDM) methods in economics: an overview, Technol. Econ. Develop. Econ., 17, 2, 397-427 (2011)
[47] Zhang, N., Method for aggregating correlated interval grey linguistic variables and its application to decision making, Technol. Econ. Develop. Econ., 19, 2, 189-202 (2013)
[48] Zhang, Z., On characterization of generalized interval type-2 fuzzy rough sets, Inform. Sci., 219, 124-150 (2013) · Zbl 1293.03029
[49] Zhang, Z.; Zhang, S., A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets, Appl. Math. Model., 37, 7, 4948-4971 (2013) · Zbl 1426.03036
[50] Zhao, T.; Xiao, J., General type-2 fuzzy rough sets based on \(α\)-plane representation theory, Soft Comput., 18, 2, 227-237 (2014) · Zbl 1325.03067
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.