1. Home
  2. Issues
  3. Volume 36, Issue 1 (2025)
  4. Two-Stage EDAS Decision Approach with Pr ...

Informatica


Two-Stage EDAS Decision Approach with Probabilistic Hesitant Fuzzy Information
Volume 36, Issue 1 (2025), pp. 65–97
Pub. online: 22 November 2024      Type: Research Article      Open accessOpen Access
Received
1 June 2024
Accepted
1 November 2024
Published
22 November 2024

Abstract

This paper develops a two-stage decision approach with probabilistic hesitant fuzzy data. Research challenges in earlier models are: (i) the calculation of occurrence probability; (ii) imputation of missing elements; (iii) consideration of attitude and hesitation of experts during weight calculation; (iv) capturing of interdependencies among experts during aggregation; and (v) ranking of alternatives with resemblance to human cognition. Driven by these challenges, a new group decision-making model is proposed with integrate methods for data curation and decision-making. The usefulness and superiority of the model is realized via an illustrative example of a logistic service provider selection.

References

 
Aldalou, E., Perçin, S. (2020). Financial performance evaluation of food and drink index using fuzzy MCDM approach. International Journal of Economics and Innovation, 6, 1–19. https://doi.org/10.20979/ueyd.650422.
 
Ashraf, S., Kousar, M., Hameed, M.S. (2023). Early infectious diseases identification based on complex probabilistic hesitant fuzzy N-soft information. Soft Computing, 27, 18285–18310. https://doi.org/10.1007/s00500-023-08083-2.
 
Bashir, Z., Rashid, T., Watróbski, J., Salabun, W., Malik, A. (2018). Hesitant probabilistic multiplicative preference relations in group decision making. Applied Sciences, 8(3), 398. https://doi.org/10.3390/app8030398.
 
Batool, B., Abosuliman, S.S., Abdullah, S., Ashraf, S. (2022). EDAS method for decision support modeling under the Pythagorean probabilistic hesitant fuzzy aggregation information. Journal of Ambient Intelligence and Humanized Computing, 13(12), 5491–5504.
 
Chen, Z., Shen, D., Ren, Y., Yu, F., Yuan, X. (2022). Airspace operation effectiveness evaluation based on q-rung orthopair probabilistic hesitant fuzzy GRA and TOPSIS. Symmetry, 14(2), 242.
 
Chen, Z., Shen, D., Yu, F., Tang, X., Zhang, Z. (2023). Multi-attribute decision-making method based on q-rung orthopair probabilistic hesitant fuzzy schweizer-sklar power weighted hamy mean operator. PloS ONE, 18(2). https://doi.org/10.1371/journal.pone.0266779.
 
Ding, J., Xu, Z., Zhao, N. (2017). An interactive approach to probabilistic hesitant fuzzy multi-attribute group decision making with incomplete weight information. Journal of Intelligent & Fuzzy Systems, 32(3), 2523–2536. https://doi.org/10.3233/JIFS-16503.
 
Divsalar, M., Ahmadi, M., Ebrahimi, E., Ishizaka, A. (2022). A probabilistic hesitant fuzzy Choquet integral-based TODIM method for multi-attribute group decision-making. Expert Systems with Applications, 191, 116266.
 
Ecer, F. (2018). Third-party logistics (3PLs) provider selection via fuzzy AHP and EDAS integrated model. Technological and Economic Development of Economy, 24(2), 615–634. https://doi.org/10.3846/20294913.2016.1213207.
 
Fang, B. (2023). Some uncertainty measures for probabilistic hesitant fuzzy information. Information Sciences, 625, 255–276.
 
Farhadinia, B., Herrera-Viedma, E. (2020). A modification of probabilistic hesitant fuzzy sets and its application to multiple criteria decision making. Iranian Journal of Fuzzy Systems, 17(4), 151–166. https://doi.org/10.22111/ijfs.2020.5412.
 
Farhadinia, B., Xu, Z. (2021). Developing the comparison techniques of probabilistic hesitant fuzzy elements in multiple criteria decision making. Soft Computing, 25, 331–342.
 
Farhadinia, B., Aickelin, U., Khorshidi, H.A. (2020). Uncertainty measures for probabilistic hesitant fuzzy sets in multiple criteria decision making. International Journal of Intelligent Systems, 35(11), 1646–1679. https://doi.org/10.1002/int.22266.
 
Feng, X., Wei, C., Liu, Q. (2018). EDAS method for extended hesitant fuzzy linguistic multi-criteria decision making. International Journal of Fuzzy Systems, 20(8), 2470–2483. https://doi.org/10.1007/s40815-018-0504-5.
 
Gao, J., Xu, Z., Liao, H. (2017). A dynamic reference point method for emergency response under hesitant probabilistic fuzzy environment. International Journal of Fuzzy Systems, 19(5), 1261–1278. https://doi.org/10.1007/s40815-017-0311-4.
 
Garg, H., Kaur, G. (2020). A robust correlation coefficient for probabilistic dual hesitant fuzzy sets and its applications. Neural Computing and Applications, 32, 8847–8866.
 
Garg, H., Krishankumar, R., Ravichandran, K.S. (2022). Decision framework with integrated methods for group decision-making under probabilistic hesitant fuzzy context and unknown weights. Expert Systems with Applications, 200, 117082. https://doi.org/10.1016/j.eswa.2022.11708 2.
 
Gong, K., Chen, C. (2019). Multiple-attribute decision making based on equivalence consistency under probabilistic linguistic dual hesitant fuzzy environment. Engineering Applications of Artificial Intelligence, 85(5), 393–401. https://doi.org/10.1016/j.engappai.2019.05.008.
 
Gong, X., Yu, C., Wu, Z. (2019). An extension of regret theory based on probabilistic linguistic cloud sets considering dual expectations: an application for the stock market. IEEE Access, 7, 171046–171060. https://doi.org/10.1109/ACCESS.2019.2956065.
 
Guo, J., Yin, J., Zhang, L., Lin, Z., Li, X. (2020). Extended TODIM method for CCUS storage site selection under probabilistic hesitant fuzzy environment. Applied Soft Computing, 93, 106381. https://doi.org/10.1016/j.asoc.2020.106381.
 
Han, J., Kamber, M., Pei, J. (2012). Data Mining: Concepts and Techniques. Elsevier.
 
Hao, Z., Xu, Z., Zhao, H., Su, Z. (2017). Probabilistic dual hesitant fuzzy set and its application in risk evaluation. Knowledge-Based Systems, 127, 16–28. https://doi.org/10.1016/j.knosys.2017.02.033.
 
Hashemkhani Zolfani, S., Ebadi Torkayesh, A., Ecer, F., Turskis, Z., Šaparauskas, J. (2021). International market selection: a MABA based EDAS analysis framework. Oeconomia Copernicana, 12(1), 99–124.
 
He, Y., Xu, Z. (2019). Multi-attribute decision making methods based on reference ideal theory with probabilistic hesitant information. Expert Systems with Applications, 118, 459–469. https://doi.org/10.1016/j.eswa.2018.10.014.
 
Hu, Y., Pang, Z. (2022). A novel similarity-based multi-attribute group decision-making method in a probabilistic hesitant fuzzy environment. IEEE Access, 10, 110410–110425.
 
Jang, M.W., Park, J.H., Son, M.J. (2023). Probabilistic picture hesitant fuzzy sets and their application to multi-criteria decision-making. AIMS Mathematics, 8(4), 8522–8559.
 
Jiang, F., Ma, Q. (2018). Multi-attribute group decision making under probabilistic hesitant fuzzy environment with application to evaluate the transformation efficiency. Applied Intelligence, 48, 953–965. https://doi.org/10.1007/s10489-017-1041-x.
 
Jiang, J., Liu, X., Wang, Z., Ding, W., Zhang, S. (2024). Large group emergency decision-making with bi-directional trust in social networks: a probabilistic hesitant fuzzy integrated cloud approach. Information Fusion, 102, 102062. https://doi.org/10.1016/j.inffus.2023.10206 2.
 
Jin, C., Mi, J., Li, F., Liang, M. (2022). A novel probabilistic hesitant fuzzy rough set based multi-criteria decision-making method. Information Sciences, 608, 489–516.
 
Jin, F., Garg, H., Pei, L., Liu, J., Chen, H. (2020). Multiplicative consistency adjustment model and data envelopment analysis-driven decision-making process with probabilistic hesitant fuzzy preference relations. International Journal of Fuzzy Systems, 22, 2319–2332.
 
Kahraman, C., Keshavarz Ghorabaee, M., Zavadskas, E.K., Cevik Onar, S., Yazdani, M., Oztaysi, B. (2017). Intuitionistic fuzzy EDAS method: an application to solid waste disposal site selection. Journal of Environmental Engineering and Landscape Management, 25(1), 1–12. https://doi.org/10.3846/16486897.2017.1281139.
 
Kao, C. (2010). Weight determination for consistently ranking alternatives in multiple criteria decision analysis. Applied Mathematical Modelling, 34(7), 1779–1787. https://doi.org/10.1016/j.apm.2009.09.022.
 
Karaşan, A., Kahraman, C. (2018). A novel interval-valued neutrosophic EDAS method: prioritization of the United Nations national sustainable development goals. Soft Computing, 22(15), 4891–4906. https://doi.org/10.1007/s00500-018-3088-y.
 
Keshavarz Ghorabaee, M., Zavadskas, E.K., Olfat, L., Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57.
 
Keshavarz Ghorabaee, M.K., Zavadskas, E.K., Amiri, M., Turskis, Z. (2016). Extended EDAS method for fuzzy multi-criteria decision-making: an application to supplier selection. International Journal of Computers Communications & Control, 11(3), 358–371.
 
Koksalmis, E., Kabak, Ö. (2019). Deriving decision makers’ weights in group decision making: an overview of objective methods. Information Fusion, 49, 146–160.
 
Lei, F., Wei, G., Shen, W., Guo, Y. (2022). PDHL-EDAS method for multiple attribute group decision making and its application to 3D printer selection. Technological and Economic Development of Economy, 28(1), 179–200.
 
Li, J., Wang, J.Q. (2017). Multi-criteria outranking methods with hesitant probabilistic fuzzy sets. Cognitive Computation, 9(5), 611–625. https://doi.org/10.1007/s12559-017-9476-2.
 
Li, J., Wang, Z. (2018a). Multi-attribute decision making based on prioritized operators under probabilistic hesitant fuzzy environments. Soft Computing, 2013, 1–20. https://doi.org/10.1007/s00500-018-3047-7.
 
Li, J., Wang, Z. (2018b). A programming model for consistency and consensus in group decision making with probabilistic hesitant fuzzy preference relations. International Journal of Fuzzy Systems, 20(8), 2399–2414. https://doi.org/10.1007/s40815-018-0501-8.
 
Li, J., Chen, Q., li Niu, L., Wang, Z. (2020a). An ORESTE approach for multi-criteria decision-making with probabilistic hesitant fuzzy information. International Journal of Machine Learning and Cybernetics, 11(7), 1591–1609. https://doi.org/10.1007/s13042-020-01060-3.
 
Li, J., Niu, L.-L., Chen, Q., Wu, G. (2020b). A consensus-based approach for multi-criteria decision making with probabilistic hesitant fuzzy information. Soft Computing, 9, 1–23. https://doi.org/10.1007/s00500-020-04886-9.
 
Li, J., Wang, J., Hu, J.-H., (2019a). Multi-criteria decision-making method based on dominance degree and BWM with probabilistic hesitant fuzzy information. International Journal of Machine Learning and Cybernetics, 10(7), 1671–1685. https://doi.org/10.1007/s13042-018-0845-2.
 
Li Y.-Y., Wang, J.-Q, Wang, T.-L. (2019b). A linguistic neutrosophic multi-criteria group decision-making approach with EDAS method. Arabian Journal for Science and Engineering, 44(3), 2737–2749. https://doi.org/10.1007/s13369-018-3487-5.
 
Liang, W., Goh, M., Wang, Y.M. (2020). Multi-attribute group decision making method based on prospect theory under hesitant probabilistic fuzzy environment. Computers and Industrial Engineering, 149(1), 1–24. https://doi.org/10.1016/j.cie.2020.106804.
 
Liao, N., Gao, H., Lin, R., Wei, G., Chen, X. (2022a). An extended EDAS approach based on cumulative prospect theory for multiple attributes group decision making with probabilistic hesitant fuzzy information. Artificial Intelligence Review, 8, 1–33.
 
Liao, N., Wei, G., Chen, X. (2022b). TODIM method based on cumulative prospect theory for multiple attributes group decision making under probabilistic hesitant fuzzy setting. International Journal of Fuzzy Systems, 24(1), 322–339.
 
Liu, S., Guo, Z. (2022). Probabilistic hesitant fuzzy multi-attribute decision-making method based on improved distance measurement. Journal of Intelligent & Fuzzy Systems, 43(5), 5953–5964.
 
Liu, P., Wu, Y., Li, Y. (2022). Probabilistic hesitant fuzzy taxonomy method based on best–worst-method (BWM) and indifference threshold-based attribute ratio analysis (ITARA) for multi-attributes decision-making. International Journal of Fuzzy Systems, 24(3), 1301–1317.
 
Liu, X.D., Wu, J., Zhang, S.T., Wang, Z.W., Garg, H. (2022). Extended cumulative residual entropy for emergency group decision-making under probabilistic hesitant fuzzy environment. International Journal of Fuzzy Systems, 24(1), 159–179.
 
Liu, X., Wang, Z., Zhang, S., Liu, J. (2020). Probabilistic hesitant fuzzy multiple attribute decision-making based on regret theory for the evaluation of venture capital projects. Economic Research-Ekonomska Istraživanja, 33(1), 672–697.
 
Maclaurin, C. (1729). A second letter to Martin Folkes, Esq., concerning the roots of Equations with demonstration of other roots of algebra. Philosopihcal Transctions Royal Society, London, Ser. A, 36, 59–96.
 
Menekse, A., Camgoz Akdag, H. (2023). A novel interval-valued spherical fuzzy EDAS: an application to IT auditor selection. In: Global Joint Conference on Industrial Engineering and Its Application Areas. Springer, Cham, pp. 3–16.
 
Mi, X., Liao, H. (2019). An integrated approach to multiple criteria decision making based on the average solution and normalized weights of criteria deduced by the hesitant fuzzy best worst method. Computers and Industrial Engineering, 133(4), 83–94. https://doi.org/10.1016/j.cie.2019.05.004.
 
Mishra, A.R., Mardani, A., Rani, P., Zavadskas, E.K. (2020). A novel EDAS approach on intuitionistic fuzzy set for assessment of health-care waste disposal technology using new parametric divergence measures. Journal of Cleaner Production, 272, 1–22.
 
Mishra, A.R., Rani, P., Pandey, K. (2022). Fermatean fuzzy CRITIC-EDAS approach for the selection of sustainable third-party reverse logistics providers using improved generalized score function. Journal of Ambient Intelligence and Humanized Computing, 13(1), 295–311.
 
Moslem, S. (2024). A novel parsimonious spherical fuzzy analytic hierarchy process for sustainable urban transport solutions. Engineering Applications of Artificial Intelligence, 128, 107447.
 
Ning, B., Lei, F., Wei, G. (2022). CODAS method for multi-attribute decision-making based on some novel distance and entropy measures under probabilistic dual hesitant fuzzy sets. International Journal of Fuzzy Systems, 24(8), 3626–3649.
 
Peng, X., Liu, C. (2017). Algorithms for neutrosophic soft decision making based on EDAS, new similarity measure and level soft set. Journal of Intelligent and Fuzzy Systems, 32(1), 955–968. https://doi.org/10.3233/JIFS-161548.
 
Qi, Q.S. (2023). TOPSIS Methods for Probabilistic hesitant fuzzy MAGDM and aplication to performance evaluation of public charging service quality. Informatica, 34(2), 317–336.
 
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(2), 495–524. https://doi.org/10.1002/int.
 
Şahin, R., Altun, F. (2020). Decision making with MABAC method under probabilistic single-valued neutrosophic hesitant fuzzy environment. Journal of Ambient Intelligence and Humanized Computing, 4, 1–23. https://doi.org/10.1007/s12652-020-01699-4.
 
Song, C., Xu, Z., Zhao, H. (2018). A novel comparison of probabilistic hesitant fuzzy elements in multi-criteria decision making. Symmetry, 10(5), 1–12. https://doi.org/10.3390/sym10050177.
 
Song, C., Xu, Z., Zhao, H. (2019). New correlation coefficients between probabilistic hesitant fuzzy sets and their applications in cluster analysis. International Journal of Fuzzy Systems, 21(2), 355–368. https://doi.org/10.1007/s40815-018-0578-0.
 
Su, Z., Xu, Z., Zhao, H., Hao, Z., Chen, B. (2019). Entropy measures for probabilistic hesitant fuzzy information. IEEE Access, 7, 65714–65727. https://doi.org/10.1109/ACCESS.2019.2916564.
 
Su, Z., Xu, Z., Zhang, S. (2024). Multi-attribute decision-making method based on probabilistic hesitant fuzzy entropy. In: Hesitant Fuzzy and Probabilistic Information Fusion: Theory and Applications. Springer Nature Singapore, Singapore, pp. 73–98.
 
Su, J., Xu, B., Jiang, L., Liu, H., Chen, Y., Li, Y. (2025). Cross-Organizational knowledge sharing partner selection based on Fogg Behavioral Model in probabilistic hesitant fuzzy environment. Expert Systems with Applications, 260, 125348.
 
Tian, X., Xu, Z., Fujita, H. (2018). Sequential funding the venture project or not? A prospect consensus process with probabilistic hesitant fuzzy preference information. Knowledge-Based Systems, 161, 172–184. https://doi.org/10.1016/j.knosys.2018.08.002.
 
Torkayesh, A.E., Deveci, M., Karagoz, S., Antucheviciene, J. (2023). A state-of-the-art survey of evaluation based on distance from average solution (EDAS): developments and applications. Expert Systems with Applications, 221, 1–22.
 
Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(2), 529–539. https://doi.org/10.1002/int.
 
Ulutaş, A., Topal, A., Görçün, Ö.F., Ecer, F. (2024). Evaluation of third-party logistics service providers for car manufacturing firms using a novel integrated grey LOPCOW-PSI-MACONT model. Expert Systems with Applications, 241, 122680.
 
Wang, Y., Jia, X., Zhang, L. (2022). Evaluation of the survival of Yangtze finless porpoise under probabilistic hesitant fuzzy environment. International Journal of Intelligent Systems, 37(10), 7665–7684.
 
Wang, Z., Li, J. (2017). Correlation coefficients of probabilistic hesitant fuzzy elements and their applications to evaluation of the alternatives. Symmetry, 9(11), 259. https://doi.org/10.3390/sym9110259.
 
Wu, J., Liu, Di, X., Wang, Z.W., Zhang, S.T. (2019). Dynamic emergency decision-making method with probabilistic hesitant fuzzy information based on GM(1, 1) and TOPSIS. IEEE Access, 7, 7054–7066. https://doi.org/10.1109/ACCESS.2018.2890110.
 
Xia, J., Chen, M., Fang, W. (2023). Normal wiggly probabilistic hesitant fuzzy set and its application in battlefield threat assessment. International Journal of Fuzzy Systems, 25(1), 145–167.
 
Xu, T.T., Zhang, H., Li, B.Q. (2022). Fuzzy entropy and hesitancy entropy in probabilistic hesitant fuzzy information and their applications. Soft Computing, 26(18), 9101–9115.
 
Xu, Z., Zhou, W. (2016). Consensus building with a group of decision makers under the hesitant probabilistic fuzzy environment. Fuzzy Optimization and Decision Making, 16(4), 1–23. https://doi.org/10.1007/s10700-016-9257-5.
 
Yang, G., Ren, M., Hao, X. (2023). Multi-criteria decision-making problem based on the novel probabilistic hesitant fuzzy entropy and TODIM method. Alexandria Engineering Journal, 68, 437–451.
 
Yanmaz, O., Turgut, Y., Can, E.N., Kahraman, C. (2020). Interval-valued Pythagorean fuzzy EDAS method: an application to car selection problem. Journal of Intelligent & Fuzzy Systems, 38(4), 4061–4077. https://doi.org/10.3233/jifs-182667.
 
Zhang, S., Gao, H., Wei, G., Wei, Y., Wei, C. (2019). Evaluation based on distance from average solution method for multiple criteria group decision making under picture 2-tuple linguistic environment. Mathematics, 7(3), 243. https://doi.org/10.3390/math7030243.
 
Zhang, S., Xu, Z., He, Y. (2017). Operations and integrations of probabilistic hesitant fuzzy information in decision making. Information Fusion, 38, 1–11. https://doi.org/10.1016/j.inffus.2017.02.001.
 
Zhang, W., Du, J., Tian, X. (2018). Finding a promising venture capital project with todim under probabilistic hesitant fuzzy circumstance. Technological and Economic Development of Economy, 24(5), 2026–2044. https://doi.org/10.3846/tede.2018.5494.
 
Zhao, P., Yue, Q., Deng, Z. (2023). Two-sided matching decision making for probabilistic hesitant fuzzy numbers. Journal of Intelligent & Fuzzy Systems, 21, 1–11.
 
Zhou, W., Xu, Z. (2017a). Expected hesitant VaR for tail decision making under probabilistic hesitant fuzzy environment. Applied Soft Computing Journal, 60, 297–311. https://doi.org/10.1016/j.asoc.2017.06.057.
 
Zhou, W., Xu, Z. (2017b). Group consistency and group decision making under uncertain probabilistic hesitant fuzzy preference environment. Information Sciences, 414, 276–288. https://doi.org/10.1016/j.ins.2017.06.004.
 
Zhou, W., Xu, Z. (2017c). Probability calculation and element optimization of probabilistic hesitant fuzzy preference relations based on expected consistency. IEEE Transactions on Fuzzy Systems, 26(3), 1367–1378.
 
Zhou, W., Liu, M., Xu, Z. (2022). Occurrence probability derivation considering different behavior strategies and decision making under the probabilistic hesitant fuzzy environment. Journal of the Operational Research Society, 74(6), 1–16.

Biographies

Krishankumar Raghunathan
raghunathan.k@iimbg.ac.in

R. Krishankumar is an assistant professor of information technology systems and analytics area, Indian Institute of Management Bodh Gaya, Bodh Gaya 824234, Bihar, India. His area of interests is multi-criteria decision-making and soft computing. He has published more than 50 articles in peer reviewed journals and is a member of the editorial board of peer-reviewed journals. He has been nominated as one of the world’s top 2% scientists based on the data from Scopus and Stanford University.

Mishra Arunodaya R.
arunodaya87@outlook.com

A.R. Mishra has PhD in mathematics, and he is lecturer in the Department of Mathematics, Govt. College Jaitwara, India. His main research interests are fuzzy sets theory, decision making, multicriteria decision making, uncertain IFSs, IVIFs, information measures, entropy measures, divergence measures and similarity and dissimilarity measures.

Rani Pratibha
pratibha138@gmail.com

P, Rani has PhD in mathematics and she is lecturer in Marwadi University, Gujarat, India. Her main research interests are fuzzy sets theory, decision making, multi-criteria decision making, uncertain IFSs, IVIFs, information measures, entropy measures, divergence measures and similarity and dissimilarity measures.

Ecer Fatih
https://orcid.org/0000-0002-6174-3241
fatihecer@gmail.com

F. Ecer received a PhD degree in operational research from the Afyon Kocatepe University, Turkey, in 2007. He is a full professor of operational research with Afyon Kocatepe, Turkey. His current research interests are in decision analysis, multiple criteria decision making (MCDM), optimization methods, artificial intelligence, artificial neural networks (ANNs), fuzzy set theory, grey set theory, soft computing, sustainability, renewable energy, transportation, and data mining. His work has been published, or is forthcoming, in high-quality international journals. As of 2022, he has an h-index of 26 (Scopus), 26 (Web of Science), and 41 (Google Scholar). Dr. Ecer has also been serving on the review board and editorial board for a number of SSCI/SCI/SCI-E/ESCI indexed journals in the world. According to Scopus and Stanford University, he has been among the world’s top 2% of scientists since 2020 in the annual and lifetime impact categories.

Zavadskas Edmundas Kazimieras
edmundas.zavadskas@vilniustech.lt

E.K. Zavadskas, PhD, DSc, Dr. habil, Dr. H. C. multi, prof. chief researcher of Institute of Sustainable Construction, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Lithuania. PhD in building structures (1973). Dr. Sc. (1987) in building technology and management. Dr. Habil (1993). Founder of Vilnius Gediminas Technical University (1990). Member of the Lithuanian Academy of Science; member of several foreign Academies of Sciences; Honorary doctor from Poznan, Saint-Petersburg, and Kyiv universities. Member of international organizations; member of steering and programme committees at many international conferences; chairman of EUROWorking Group ORSDCE; associate editor, guest editor, or editorial board member for 40 international journals (Computers-Aided Civil and Infrastructure Engineering, Automation in Construction, Informatica, International Journal of Information Technology and Decision Making, Archives of Civil and Mechanical Engineering, International Journal of Fuzzy Systems, Symmetry, Sustainability, Applied Intelligence, Energy, Entropy and other); author and co-author of more than 600 papers and a number of monographs in Lithuanian, English, German and Russian. Founding editor of journals Technological and Economic Development of Economy, Journal of Civil Engineering and Management, International Journal of Strategic Property Management. He was a highly cited researcher in 2014, 2018, 2019, 2020. Research interests: multi-criteria decision making, civil engineering, sustainable development, fuzzy multi-criteria decision making.

Ravichandran Kattur Soundarapandian
ravichandran20962@gmail.com

K.S. Ravichandran recently joined AmritaVishwaVidyapeetham in Coimbatore as a distinguished professor in the Department of Mathematics at the Amrita School of Physical Sciences, Tamil Nadu, India. Prior to this appointment, he held the position of the registrar at Rajiv Gandhi National Institute of Youth Development (RGNIYD), an Institute of National Importance under the Government of India located in Sriperumbudur, Kancheepuram. Additionally, he served as an associate dean of research at SASTRA University in Thanjavur, India. After obtaining his master’s degree in computer applications (MCA) and master of science in mathematics (MSc), prof. Ravichandran completed his PhD in mathematics at Alagappa University, Tamil Nadu, India. His academic contributions are notable, with a publication record of more than 200 research articles. Among these, over 185 are indexed in SCOPUS, and more than 95 are indexed in SCI/SCIE/ABDC journals, boasting an average impact factor exceeding 4.25 and an H-index of 31. Professor Ravichandran specializes in various domains such as medical image processing, machine learning, deep learning, multi-criteria decision-making, and computational intelligence and its applications. He completed two research-funded projects as principle investigator worth INR 78 Lakhs. He currently serves as an associate editor for the International Journal of Information Technology, a SCOPUS-indexed journal published by Springer. Moreover, he actively contributes as a reviewer for over 50 SCI/SCIE-indexed journals.

Gandomi Amir H.
gandomi@uts.edu.au

A.H. Gandomi is a professor of data science and an ARC DECRA fellow at the Faculty of Engineering & Information Technology, University of Technology Sydney. He is also affiliated with Obuda University, Budapest, as a Distinguished Professor. Prior to joining UTS, prof. Gandomi was an assistant professor at the Stevens Institute of Technology and a distinguished research fellow at BEACON Center, Michigan State University. Prof. Gandomi has published over three hundred journal papers and 12 books, which have collectively been cited 60,000 times. He has been named as one of the most influential scientific minds and received the Highly Cited Researcher award (top 1% publications and 0.1% researchers) from Web of Science for six years. In a recent study at Stanford University, released by Elsevier, prof. Amir H. Gandomi is ranked 24th most impactful researcher in the AI and Image Processing subfield in 2023! He also ranked 18th in GP bibliography among more than 17,000 researchers. He has received multiple prestigious awards for his research excellence and impact, such as the 2024 IEEE TCSC Award for Excellence in Scalable Computing (MCR), the 2023 Achenbach Medal, and the 2022 Walter L. Huber Prize, the highest-level mid-career research award in all areas of civil engineering. He has served as associate editor, editor, and guest editor in several prestigious journals, such as AE of IEEE Networks and IEEE IoTJ. Prof. Gandomi is active in delivering keynotes and invited talks. His research interests are global optimisation and (big) data analytics using machine learning and evolutionary computations in particular.


Full article Related articles PDF XML
Full article Related articles PDF XML

Copyright
© 2025 Vilnius University
Open access article under the CC BY license.

Keywords
case-based approach EDAS entropy measure group decision-making Regret theory Maclaurin symmetric mean

Metrics
since January 2020
244

Article info
views

388

Full article
views

137

PDF
downloads

37

XML
downloads


Share


RSS