Intuitionistic Fuzzy Multi-attribute Decision-making Based on the New Entropy and Improved TOPSIS
- DOI
- 10.2991/978-94-6463-570-6_130How to use a DOI?
- Keywords
- intuitionistic fuzzy sets; intuitionistic fuzzy entropy; improved TOPSIS; grey correlation
- Abstract
Given the limitations of current research on intuitionistic fuzzy entropy, which often overlook the hesitancy and uncertainty degrees, this paper introduces a novel intuitionistic fuzzy entropy that accounts for both deviation and hesitancy degrees. Subsequently, a multi-attribute decision-making model is developed, incorporating this new entropy and an enhanced TOPSIS method. The attribute weights are derived using both the entropy weight method and an optimal model that minimizes entropy. To improve the TOPSIS method, grey relational analysis is employed instead of the traditional distance from the positive-negative ideal solution, measuring the closeness of alternatives to these ideal solutions. Finally, two examples are provided to demonstrate the effectiveness of our proposed method.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Qiqing Wang AU - Jiahang Yuan AU - Cunbin Li PY - 2024 DA - 2024/11/22 TI - Intuitionistic Fuzzy Multi-attribute Decision-making Based on the New Entropy and Improved TOPSIS BT - Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024) PB - Atlantis Press SP - 1298 EP - 1308 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-570-6_130 DO - 10.2991/978-94-6463-570-6_130 ID - Wang2024 ER -