Research on Two-Stage Hesitate Fuzzy Information Fusion Framework Incorporating Prospect Theory and Dichotomy Algorithm

In order to control the systematic divergence among decision makers (DMs) and preserve the original decision preference, this paper proposes a novel decision information fusion framework under the hesitant fuzzy environment. First, a maximum compactness-based normalization method is presented to nor...

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Bibliographic Details
Published inInternational journal of fuzzy systems Vol. 24; no. 3; pp. 1530 - 1547
Main Authors Tao, Xiwen, Jiang, Wenqi
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2022
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Computational Intelligence
Data integration
Decision analysis
Decision making
Decision theory
Efficiency
Engineering
Feedback
Fuzzy logic
Fuzzy sets
Management Science
Operations Research
Optimization
Preferences
Statistical inference
Prospect theory
Group consensus
Hesitant fuzzy element
Dichotomy algorithm
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Summary:In order to control the systematic divergence among decision makers (DMs) and preserve the original decision preference, this paper proposes a novel decision information fusion framework under the hesitant fuzzy environment. First, a maximum compactness-based normalization method is presented to normalize hesitant fuzzy elements (HFEs) as pretreatment of decision data. Second, prospect theory is introduced to assign the optimal aggregation weights to maximize the efficiency of the preference aggregation process, in which the expected consensus threshold is viewed as a reference point estimated through statistic inference to distinguish DMs’ status. Third, an effective feedback mechanism is designed to improve group consensus, and the dichotomy algorithm is utilized to search optimal feedback weight to preserve original decision information. Finally, a case study and comparison analysis are illustrated to show the efficiency of the proposed hesitant fuzzy information fusion method.
ISSN:1562-2479
2199-3211
DOI:10.1007/s40815-021-01207-6