The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets
Abstract
Recently, a new model based on Pythagorean fuzzy set (PFS) has been presented to manage the uncertainty in real-world decision-making problems. PFS has much stronger ability than intuitionistic fuzzy set to model such uncertainty. In this paper, we define some novel operational laws of PFSs and discuss their desirable properties. For the multicriteria decision-making problems with PFSs, we propose an extended technique for order preference by similarity to ideal solution method to deal effectively with them. In this approach, we first propose a score function based comparison method to identify the Pythagorean fuzzy positive ideal solution and the Pythagorean fuzzy negative ideal solution. Then, we define a distance measure to calculate the distances between each alternative and the Pythagorean fuzzy positive ideal solution as well as the Pythagorean fuzzy negative ideal solution, respectively. Afterward, a revised closeness is introduced to identify the optimal alternative. At length, a practical example is given to illustrate the developed method and to make a comparative analysis.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Atanassov, K. T. (1986) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(86)80034-3 · Cited by 16,465
Multiple Attribute Decision Making
Hwang, C.-L., & Yoon, K. (1981) · Lecture Notes in Economics and Mathematical Systems · doi:10.1007/978-3-642-48318-9 · Cited by 8,526
Decision-Making in a Fuzzy Environment
Bellman, R. E., & Zadeh, L. A. (1970) · Management Science · doi:10.1287/mnsc.17.4.b141 · Cited by 6,813
Decision Making in a Fuzzy Environment
(2007) · Advances in Fuzzy Systems — Applications and Theory · doi:10.1142/9789812770622_0004 · Cited by 5,320
Extensions of the TOPSIS for group decision-making under fuzzy environment
Chen, C.-T. (2000) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(97)00377-1 · Cited by 3,844
Pythagorean Membership Grades in Multicriteria Decision Making
Yager, R. R. (2014) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2013.2278989 · Cited by 2,847
Yager, R. R. (2013) · 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS) · doi:10.1109/ifsa-nafips.2013.6608375 · Cited by 2,703
Intuitionistic Fuzzy Aggregation Operators
Xu, Z. (2007) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2006.890678 · Cited by 2,678
Self-citationSome geometric aggregation operators based on intuitionistic fuzzy sets
Xu, Z., & Yager, R. R. (2006) · International Journal of General Systems · doi:10.1080/03081070600574353 · Cited by 2,356
Self-citationGau, W.-L., & Buehrer, D. J. (1993) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.229476 · Cited by 1,644
Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.03.039 · Cited by 1,500
Yager, R. R., & Abbasov, A. M. (2013) · International Journal of Intelligent Systems · doi:10.1002/int.21584 · Cited by 1,430
FUZZY MEASURES AND FUZZY INTEGRALS—A SURVEY
Sugeno, M. (1993) · Readings in Fuzzy Sets for Intelligent Systems · doi:10.1016/b978-1-4832-1450-4.50027-4 · Cited by 767
Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information
Xu, Z., & Zhang, X. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.05.011 · Cited by 627
Self-citationDynamic intuitionistic fuzzy multi-attribute decision making
Xu, Z., & Yager, R. R. (2008) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2007.08.008 · Cited by 594
Self-citationON THE MEASURE OF FUZZINESS AND NEGATION Part I: Membership in the Unit Interval
YAGER, R. R. (1979) · International Journal of General Systems · doi:10.1080/03081077908547452 · Cited by 590
Shen, L., Olfat, L., Govindan, K., Khodaverdi, R., & Diabat, A. (2013) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2012.09.006 · Cited by 471
Intuitionistic Fuzzy Bonferroni Means
Xu, Z., & Yager, R. R. (2011) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2072918 · Cited by 442
Self-citationThe interval-valued fuzzy TOPSIS method and experimental analysis
Chen, T.-Y., & Tsao, C.-Y. (2008) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2007.11.004 · Cited by 409
Atanassov, K., Pasi, G., & Yager, R. (2005) · International Journal of Systems Science · doi:10.1080/00207720500382365 · Cited by 336
A VIKOR-based method for hesitant fuzzy multi-criteria decision making
Liao, H., & Xu, Z. (2013) · Fuzzy Optimization and Decision Making · doi:10.1007/s10700-013-9162-0 · Cited by 334
Self-citationThe TODIM analysis approach based on novel measured functions under hesitant fuzzy environment
Zhang, X., & Xu, Z. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.02.006 · Cited by 234
Self-citationOn the measure of fuzziness and negation. II. Lattices
Yager, R. R. (1980) · Information and Control · doi:10.1016/s0019-9958(80)90156-4 · Cited by 232
A modified VIKOR multiple-criteria decision method for improving domestic airlines service quality
Liou, J. J. H., Tsai, C.-Y., Lin, R.-H., & Tzeng, G.-H. (2011) · Journal of Air Transport Management · doi:10.1016/j.jairtraman.2010.03.004 · Cited by 223
Fuzzy inferior ratio method for multiple attribute decision making problems
Hadi‐Vencheh, A., & Mirjaberi, M. (2014) · Information Sciences · doi:10.1016/j.ins.2014.02.019 · Cited by 150
Cited by
Generalized Orthopair Fuzzy Sets
Yager, R. R. (2017) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2016.2604005 · Cited by 2,251
Spherical fuzzy sets and spherical fuzzy TOPSIS method
Kutlu Gündoğdu, F., & Kahraman, C. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-181401 · Cited by 965
Ilbahar, E., Karaşan, A., Cebi, S., & Kahraman, C. (2018) · Safety Science · doi:10.1016/j.ssci.2017.10.025 · Cited by 573
Fundamental Properties of Interval-Valued Pythagorean Fuzzy Aggregation Operators
Peng, X., & Yang, Y. (2016) · International Journal of Intelligent Systems · doi:10.1002/int.21790 · Cited by 537
Zavadskas, E. K., Mardani, A., Turskis, Z., Jusoh, A., & Nor, K. M. (2016) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622016300019 · Cited by 485
Metadata
- Authors
- Zhang, X., & Xu, Z.
- Venue
- International Journal of Intelligent Systems
- Publisher
- Wiley
- Published
- 2014-10-13
- Volume
- 29
- Issue
- 12
- Pages
- 1061-1078
- ISSN
- 0884-8173 (ISSN-L)
- OpenAlex
- W1829951598
- Cited by
- 1,506
- References
- 25
- FWCI
- 35.32
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.