A Group Decision-making Method Based on Evidence Theory and Deng Entropy
- DOI
- 10.2991/978-94-6463-690-1_3How to use a DOI?
- Keywords
- Group decision-making; Deng entropy; Evidence theory; CREAM
- Abstract
As group decision-making problems become increasingly complex and the decision-making environment continues to change, a large amount of uncertain information emerges in the decision-making process. How to express and deal with the uncertainty to make the decision results more reasonable is particularly important. In this paper, a group decision-making method based on Evidence theory and Deng entropy is proposed. Firstly, the uncertainty of expert opinions is expressed using Evidence theory through the employment of basic probability assignments (BPAs). Secondly, the uncertainty degree of each BPA is measured by Deng entropy. Thirdly, the BPAs are fused by considering their uncertainty degree and converted into probability distributions through the pignistic probability transformation function. Finally, a decision is made. A case study is provided to demonstrate the effectiveness of the proposed method.
- Copyright
- © 2025 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 - Ziying Hong AU - Jian Zhong AU - Xiaoyan Su PY - 2025 DA - 2025/04/23 TI - A Group Decision-making Method Based on Evidence Theory and Deng Entropy BT - Proceedings of the 2024 6th International Conference on Economic Management and Model Engineering (ICEMME 2024) PB - Atlantis Press SP - 13 EP - 21 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-690-1_3 DO - 10.2991/978-94-6463-690-1_3 ID - Hong2025 ER -