Abstract
The evaluated data with multiplicative or linguistic preferences should be aggregated with information fusion and aggregation that are important research topics in many fields, such as neural networks, fuzzy logic controllers, expert systems, group decision-making, and multi-criteria decision-making (MCDM). Ordered weighted averaging operators have been extensively adopted to handle MCDM problems. However, previous operators are usually independent of their situations and cannot reflect the change in decision situations. Besides, how to solve MCDM problems with feasible operators is still an interesting direction. To resolve above problems, a linguistic MCDM aggregation model is proposed in this paper, which contained two linguistic and situational operators, that is, situational maximal entropy linguistic ordered weighted averaging and maximal entropy linguistic ordered weighted geometric averaging operators. This proposed model has their ability to handle MCDM problems under different decision situations according to the decision-maker’s preference toward criteria. The proposed model is applied to two previous datasets, which are the evaluation of the best main battle tank and the best school in a university. The results of this paper indicate that the proposed model can deal with the situational group MCDM problems with linguistic or multiplicative and linguistic preferences based on proposed operators.
Similar content being viewed by others
References
Azcel J, Saaty TL (1983) Procedures for synthesizing ratio judgments. J Math Psychol 27(1):93–102
Beliakov G, Warren J (2001) Appropriate choice of aggregation operators in fuzzy decision support systems. IEEE Trans Fuzzy Syst 9(6):773–784
Canos L, Liern V (2008) Soft computing-based aggregation methods for human resource management. Eur J Oper Res 189:669–681
Chang J-R, Yu P-Y (2019) Weighted-fuzzy-relations time series for forecasting information technology maintenance cost. Granul Comput 4:687–697
Chang J-R, Ho T-H, Cheng C-H, Chen A-P (2006) Dynamic fuzzy OWA model for group multiple criteria decision making. Soft Comput 10(7):543–554
Chen S-M, Chiou C-H (2014) Multiattribute decision making based on interval-valued intuitionistic fuzzy sets, PSO techniques, and evidential reasoning methodology. IEEE Trans Fuzzy Syst 23(6):1905–1916
Chen S-M, Chu Y-C (2020) Multiattribute decision making based on U-quadratic distribution of intervals and the transformed matrix in interval-valued intuitionistic fuzzy environments. Inf Sci 537:30–45
Chen S-M, Hong J-A (2014) Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets and the TOPSIS method. IEEE Trans Syst Man Cybern Syst 44(12):1665–1673
Chen SM, Huang ZC (2017) Multiattribute decision making based on interval-valued intuitionistic fuzzy values and linear programming methodology. Inf Sci 381:341–351
Cheng C-H, Chang J-R (2006) MCDM aggregation model using situational ME-OWA and ME-OWGA operators. Int J Uncertain Fuzziness Knowl Based Syst 14(4):421–443
Cheng C-H, Lin Y (2002) Evaluating the best battle tank using fuzzy decision theory with linguistic criteria evaluation. Eur J Oper Res 142:174–186
Cheng C-H, Chang J-R, Ho T-H, Chen A-P (2006) Dynamic fuzzy owa model for evaluating the risks of software development. Cybern Syst 37(8):791–813
Chiclana F, Herrera F, Herrera-Viedma E (2001) Integrating multiplicative preference relations in a multipurpose decision-making model based on fuzzy preference relations. Fuzzy Sets Syst 122:277–291
Chiclana F, Herrera-Viedma E, Herrera F, Alonso S (2007) Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations. Eur J Oper Res 182:383–399
Choi DY (1999) A new aggregation method in a fuzzy environment. Decis Support Syst 25:39–51
Fahmi A, Abdullah S, Amin F (2021) Aggregation operators on cubic linguistic hesitant fuzzy numbers and their application in group decision-making. Granul Comput 6:303–320
Feng F, Fujita H, Ali MI, Yager RR, Liu X (2019) Another view on generalized intuitionistic fuzzy soft sets and related multiattribute decision making methods. IEEE Trans Fuzzy Syst 27(3):474–488
Feng F, Zheng Y, Sun B, Akram M (2022) Novel score functions of generalized orthopair fuzzy membership grades with application to multiple attribute decision making. Granul Comput 7:95–111
Fuller R, Majlender P (2001) An analytic approach for obtaining maximal entropy OWA operator weights. Fuzzy Sets Syst 124:53–57
Harmati IÁ, Fullér R, Felde I (2022) On stability of maximal entropy OWA operator weights. Fuzzy Sets Syst. https://doi.org/10.1016/j.fss.2022.01.003
Herrera F, Herrera-Viedma E (2003) A study of the origin and uses of the ordered weighted geometric operator in multicriteria decision making. Int J Intell Syst 18:689–707
Herrera F, Herrera-Viedma E, Chiclana F (2001) Multiperson decision-making based on multiplicative preference relations. Eur J Oper Res 129:372–385
Jaynes ET (1989) Cleaning up mysteries: the original goal, maximum entropy and Bayesian methods. Kluwer, Dordrecht
Jiang Y, Xu Z, Shu Y (2017) Interval-valued intuitionistic multiplicative aggregation in group decision making. Granul Comput 2:387–407
Khan MSA, Abdullah S, Ali A, Amin F, Rahman K (2019) Hybrid aggregation operators based on Pythagorean hesitant fuzzy sets and their application to group decision making. Granul Comput 4:469–482
Klir GJ (1988) Fuzzy sets, uncertainly and information. Prentice Hall, Hoboken
Liu P, Chen S-M, Tang G (2019) Multicriteria decision making with incomplete weights based on 2-D uncertain linguistic Choquet integral operators. IEEE Trans Cybern 51(4):1860–1874
Mesiar R (2007) Fuzzy set approach to the utility, preference relations, and aggregation operators. Eur J Oper Res 176:414–422
Moshkovich HM, Schellenberger RE, Olson DL (1998) Data influences the result more than preferences: some lessons from implementation of multiattribute techniques in a real decision task. Decis Support Syst 22:73–84
O’Hagan M (1988) Aggregating template or rule antecedents in real-time expert systems with fuzzy set logic. In: Proceedings of 22nd annual IEEE asilomar conference on signals, systems, computers, Pacific Grove, CA, pp 681–689
Rahman K, Abdullah S (2019) Generalized interval-valued Pythagorean fuzzy aggregation operators and their application to group decision-making. Granul Comput 4:15–25
Smolikova R, Wachowiak MP (2002) Aggregation operators for selection problems. Fuzzy Sets Syst 131:23–34
Tang J, Meng F (2019) Linguistic intuitionistic fuzzy Hamacher aggregation operators and their application to group decision making. Granul Comput 4:109–124
Tao Z, Liu X, Chen H, Liu J (2014) Entropy measures for linguistic information and its application to decision making. J Intell Fuzzy Syst. https://doi.org/10.3233/IFS-141487
Torra V, Cortes U (1995) Towards and automatic consensus generator tool: EGAC. IEEE Trans Syst Man Cybern 25:888–894
Wang C-Y, Chen S-M (2017) Multiple attribute decision making based on interval-valued intuitionistic fuzzy sets, linear programming methodology, and the extended TOPSIS method. Inf Sci 397–398:155–167
Xu ZS (2004a) A method based on linguistic aggregation operators for group decision making with linguistic preference relations. Inf Sci 166:19–30
Xu ZS (2004b) Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment. Inf Sci 168:171–184
Xu ZS, Da QL (2002) The Ordered weighted geometric averaging operators. Int J Intell Syst 17:709–716
Xu Z, Wang H (2016) Managing multi-granularity linguistic information in qualitative group decision making: an overview. Granul Comput 1:21–35
Yager RR (1980) On a general class of fuzzy connectives. Fuzzy Sets Syst 4:235–242
Yager RR (1988) Ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans Syst Man Cybern 18(1):183–190
Yager RR (1991) Connectives and quantifiers in fuzzy sets. Fuzzy Sets Syst 40:39–75
Yager RR (2018) Decision making under measure-based granular uncertainty. Granul Comput 3:345–353
Yager RR, Kacprzyk J (1997) The ordered weighted averaging operators. Kluwer Academic Publishers, Boston
Yoon KP, Hwang C-L (1995) Multiple attribute decision making: an introduction. Sage Publications Inc, California
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning, parts 1 and 2. Inf Sci 8:199–249 (301–357)
Zadeh LA (1976) The concept of a linguistic variable and its application to approximate reasoning, parts 3. Inf Sci 9:43–80
Zeng S, Chen S-M, Kuo L-W (2019) Multiattribute decision making based on novel score function of intuitionistic fuzzy values and modified VIKOR method. Inf Sci 488:76–92
Zeng S, Chen S-M, Fan K-Y (2020) Interval-valued intuitionistic fuzzy multiple attribute decision making based on nonlinear programming methodology and TOPSIS method. Inf Sci 506:424–442
Zimmermann HJ, Zysno P (1980) Latent connectives in human decision making. Fuzzy Sets Syst 4:37–51
Zou X-Y, Chen S-M, Fan K-Y (2020) Multiple attribute decision making using improved intuitionistic fuzzy weighted geometric operators of intuitionistic fuzzy values. Inf Sci 535:242–253
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cheng, CH., Chen, MY. & Chang, JR. Linguistic multi-criteria decision-making aggregation model based on situational ME-LOWA and ME-LOWGA operators. Granul. Comput. 8, 97–110 (2023). https://doi.org/10.1007/s41066-022-00316-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41066-022-00316-3