TOPSIS Method Based on Prospect Theory for Interval Value Hesitant Fuzzy Set MAGDM and Its Application to Digital Economy
Authors: 
Qi Wang, Zhiping WangAuthors Info & Claims
Pages 319 - 325
Abstract
In the information age, the digital economy is increasingly becoming the critical driving force of development, and research on the digital economy has also become popular, but research on the combination of the digital economy and the multi-attribute group decision-making (MAGDM) is relatively lacking. This paper combines the two to study the application of the MAGDM based on the interval value hesitant fuzzy sets (IVHFSs) in the digital economy. Aiming at the issue that the information evaluation of decision makers (DMs) is the interval-value hesitant fuzzy element (IVHFE), this paper proposes a MAGDM method based on prospect theory and the technique for order preference by similarity to an ideal solution (TOPSIS). Firstly, the weight determination model is constructed based on the score function. Secondly, on the premise of fully considering the psychological behavior of DMs, the positive and negative ideal solution matrices are solved, and the advantages and disadvantages of multiple alternatives are ranked based on the TOPSIS method. Finally, the superiority of the proposed method is verified by comparison.
References
[1]
Elena, P.: Transformation of business models of logistics and transportation companies in digital economy. Transportation ResearchProcedia. 63, 2130-2137 (2022).
[2]
Zhen, X.Y., Yu, D.J.: Human resource management performance evaluation model based on improved multi-attribute group decision making. Microcomputer application. 38 (07), 38-42 (2022). (In Chinese)
[3]
Zadeh, L.A.: Fuzzy sets. Information and Control. 8 (3), 338-353 (1965).
[4]
Atanassov, K.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87-96 (1986).
[5]
Torra, V.: Hesitant fuzzy sets. International Journal of Intelligent Systems. 25 (6), 529-539 (2010).
[6]
Chen, N.: Interval-valued hesitant preference relations and their applications to group decision making. Knowledge Based Systems. 528-540 (2013).
[7]
Zhang, S., X, Z.S., He, Y.: Operations and integrations of probabilistic hesitant fuzzy information in decision making. Information Fusion. 38, 1-11 (2017).
[8]
Chen, S.W., Cai, L.N.: Interval valued hesitant fuzzy sets. Fuzzy System and Mathematics. 27 (06), 38-44 (2013). (In Chinese)
[9]
Luo, C., Ju, Y.B., Ernesto D.R.: The waste-to-energy incineration plant site selection based on hesitant fuzzy linguistic Best-Worst method ANP and double parameter TOPSIS approach. A case study in China, Energy. 211, 118564 (2020).
[10]
Hu, M.J., Lan, J.B., Wang, Z.G.: A distance measure, similarity measure and possibility degree for hesitant interval-valued fuzzy sets. Computers & Industrial Engineering. 137, 106088 (2019).
[11]
Na, C., Xu, Z.S.: Interval-valued hesitant preference relations and their applications to group decision making. Knowledge-Based Systems. 37, 528-540 (2013).
[12]
Kullback, S., Leibler, R.A.: On information and sufficiency. Annals of Mathematical Statistics. 22 (1), 79-86 (1951).
[13]
Kahneman, D., Tversky, A.: Prospect theory: An analysis of decision under risk. Handbook of the Fundamentals of Financial Decision Making. 99-127 (2013).
[14]
Liang, W., Mark Goh, Wang, Y.M.: Multi-attribute group decision making method based on prospect theory under hesitant probabilistic fuzzy environment, Computers & Industrial Engineering, 149, 106804, (2020).
[15]
Mao, X.B., M, W., Dong, J.Y.: A new method for probabilistic linguisticmulti -attribute group decision making: application to the selection of financial technologies. Applied Soft Computing Journal. 77, 1-8 (2019).
[16]
Liang, W., Mark G., Wang, Y.M.: Multi-attribute group decision making method based on prospect theory under hesitant probabilistic fuzzy environment, Computers & Industrial Engineering, 149, 106804, (2020).
[17]
Liao, Z.Q., Liao, H.C., Gou, X.J.: A hesitant fuzzy linguistic choquet integral-based multimoora method for multiple criteria decision making and its application in talent selection. Economic Computation and Economic Cybernetics Studies and Research. 53 (2) (2019).
[18]
Corrado, l.S., Pietro, E.: Infrastructure efficiency, logistics quality and environmental impact of land logistics systems in the EU: A DEA-based dynamic mapping. Research in Transportation Business & Management. (2022). https://doi.org/10.1016/j.rtbm.2022.100814.
[19]
Liang, W., Wang, Y.M.: Interval Hesitative Fuzzy TOPSIS Decision Making Method Considering Group Consistency. Systems Science and Mathematics. 07, 1146-1156 (2022). (In Chinese)
Index Terms
- TOPSIS Method Based on Prospect Theory for Interval Value Hesitant Fuzzy Set MAGDM and Its Application to Digital Economy
Recommendations
Hesitant fuzzy linguistic TOPSIS method using a possibility-based comparison approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets
The purpose of this study is to develop a hesitant fuzzy linguistic TOPSIS (The technique for order preference by similarity to ideal solution) method with a possibility-based comparison approach for addressing multi-criteria decision-making (MCDM) ...
Multi-criteria decision-making using interval-valued hesitant fuzzy QUALIFLEX methods based on a likelihood-based comparison approach
QUALIFLEX is a very efficient outranking method to handle multi-criteria decision-making (MCDM) involving cardinal and ordinal preference information. Based on a likelihood-based comparison approach, this paper develops two interval-valued hesitant ...
Integrating LINMAP and TOPSIS methods for hesitant fuzzy multiple attribute decision making
The linear programming technique for multidimensional analysis of preference LINMAP and the technique for order preference by similarity to ideal solution TOPSIS are two well-known methods for dealing with the multiple attribute decision making MADM ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
September 2023
441 pages
ISBN:9798400707667
DOI:10.1145/3627377
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 December 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICBDT 2023
ICBDT 2023: 2023 6th International Conference on Big Data Technologies
September 22 - 24, 2023
Qingdao, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 12Total Downloads
- Downloads (Last 12 months)8
- Downloads (Last 6 weeks)0
Reflects downloads up to 09 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format