Version [1.1]- [Handling decision-making in intuitionistic fuzzy environment: PyIFDM package]
The paper introduces the update of the Python Intuitionistic Fuzzy Decision Making (PyIFDM) package, emphasizing its focus on Multi-Criteria Decision Analysis (MCDA) with uncertain data modeled as Intuitionistic Fuzzy Sets (IFS). The update enhances PyIFDM’s capabilities, introducing five new IF-MCD...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-02-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711023003163 |
| _version_ | 1797366584992333824 |
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| author | Jakub Więckowski Bartłomiej Kizielewicz Witold Chmielarz Wojciech Sałabun |
| author_facet | Jakub Więckowski Bartłomiej Kizielewicz Witold Chmielarz Wojciech Sałabun |
| author_sort | Jakub Więckowski |
| collection | DOAJ |
| description | The paper introduces the update of the Python Intuitionistic Fuzzy Decision Making (PyIFDM) package, emphasizing its focus on Multi-Criteria Decision Analysis (MCDA) with uncertain data modeled as Intuitionistic Fuzzy Sets (IFS). The update enhances PyIFDM’s capabilities, introducing five new IF-MCDA methods and basic mathematical operations on IFS. The update includes the implementation of Max normalization, a Hausdorff measure-based Euclidean distance, and a module for IFS-similarity measures. The release contributes to a significant library upgrade, providing advanced tools for modeling fuzzy data in and outside the MCDA space. PyIFDM now supports a greater variety of popular IF-MCDA methods, offering flexibility and applicability in decision-making scenarios. The added visualization module facilitates the graphical representation of IFS, aiding the interpretation of complex decision models involving intuitionistic fuzzy data. The enhanced version of the PyIFDM library expands its applicability in research methodologies, making it a more efficient tool for conducting multi-criteria decision analysis in an intuitionistic fuzzy environment. |
| first_indexed | 2024-03-08T17:06:49Z |
| format | Article |
| id | doaj.art-5bf9106712114f468590bbbe214c1e51 |
| institution | Directory Open Access Journal |
| issn | 2352-7110 |
| language | English |
| last_indexed | 2024-03-08T17:06:49Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj.art-5bf9106712114f468590bbbe214c1e512024-01-04T04:39:40ZengElsevierSoftwareX2352-71102024-02-0125101620Version [1.1]- [Handling decision-making in intuitionistic fuzzy environment: PyIFDM package]Jakub Więckowski0Bartłomiej Kizielewicz1Witold Chmielarz2Wojciech Sałabun3National Institute of Telecommunications, Szachowa 1, 04-894 Warsaw, PolandNational Institute of Telecommunications, Szachowa 1, 04-894 Warsaw, PolandFaculty of Management, University of Warsaw, 00-927 Warsaw, PolandNational Institute of Telecommunications, Szachowa 1, 04-894 Warsaw, Poland; Research Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, 71-210 Szczecin, Poland; Corresponding author at: Research Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, 71-210 Szczecin, Poland.The paper introduces the update of the Python Intuitionistic Fuzzy Decision Making (PyIFDM) package, emphasizing its focus on Multi-Criteria Decision Analysis (MCDA) with uncertain data modeled as Intuitionistic Fuzzy Sets (IFS). The update enhances PyIFDM’s capabilities, introducing five new IF-MCDA methods and basic mathematical operations on IFS. The update includes the implementation of Max normalization, a Hausdorff measure-based Euclidean distance, and a module for IFS-similarity measures. The release contributes to a significant library upgrade, providing advanced tools for modeling fuzzy data in and outside the MCDA space. PyIFDM now supports a greater variety of popular IF-MCDA methods, offering flexibility and applicability in decision-making scenarios. The added visualization module facilitates the graphical representation of IFS, aiding the interpretation of complex decision models involving intuitionistic fuzzy data. The enhanced version of the PyIFDM library expands its applicability in research methodologies, making it a more efficient tool for conducting multi-criteria decision analysis in an intuitionistic fuzzy environment.http://www.sciencedirect.com/science/article/pii/S2352711023003163PythonIntuitionistic fuzzy MCDADecision-makingIntuitionistic fuzzy setsUncertain data |
| spellingShingle | Jakub Więckowski Bartłomiej Kizielewicz Witold Chmielarz Wojciech Sałabun Version [1.1]- [Handling decision-making in intuitionistic fuzzy environment: PyIFDM package] SoftwareX Python Intuitionistic fuzzy MCDA Decision-making Intuitionistic fuzzy sets Uncertain data |
| title | Version [1.1]- [Handling decision-making in intuitionistic fuzzy environment: PyIFDM package] |
| title_full | Version [1.1]- [Handling decision-making in intuitionistic fuzzy environment: PyIFDM package] |
| title_fullStr | Version [1.1]- [Handling decision-making in intuitionistic fuzzy environment: PyIFDM package] |
| title_full_unstemmed | Version [1.1]- [Handling decision-making in intuitionistic fuzzy environment: PyIFDM package] |
| title_short | Version [1.1]- [Handling decision-making in intuitionistic fuzzy environment: PyIFDM package] |
| title_sort | version 1 1 handling decision making in intuitionistic fuzzy environment pyifdm package |
| topic | Python Intuitionistic fuzzy MCDA Decision-making Intuitionistic fuzzy sets Uncertain data |
| url | http://www.sciencedirect.com/science/article/pii/S2352711023003163 |
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