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A novel technique for multiple attribute group decision making in interval-valued hesitant fuzzy environments with incomplete weight information

  • Chunqin Zhang
  • Chao WangEmail author
  • Zhiming Zhang
  • Dazeng Tian
Original Research

Abstract

We propose a new technique for handling multiple attribute group decision making (MAGDM) problems in interval-valued hesitant fuzzy (IVHF) environments with imperfect weight information. Firstly, the quadratic programming model is given to acquire the weights of decision makers by utilizing maximum group consensus between individual and group IVHF decision matrices. Then, the maximum deviation method is employed to build an optimum model, where the best weights for attributes are obtained. Subsequently, an IVHF–TOPSIS approach is developed to obtain a solution that simultaneously has the smallest distance from the IVHF-positive ideal solution (IVHFPIS) and the largest distance from the IVHF-negative ideal solution (IVHFNIS). Ultimately, the novel method is verified with an investment example.

Keywords

Interval-valued hesitant fuzzy set (IVHFS) Group decision making Maximum group consensus method Maximum deviation method TOPSIS 

Notes

Acknowledgements

The authors thank the anonymous referees for their valuable suggestions in improving this paper. This work was supported by the National Natural Science Foundation of China (Nos. 11626079 and 61672205), the Natural Science Foundation of Hebei Province of China (No. F2015402033), the Scientific Research Project of Department of Education of Hebei Province of China (Nos. BJ2017031, QN2018161, and QN2016235), and the Natural Science Foundation of Hebei University (Nos. 799207217073 and 799207217108).

Compliance with ethical standards

Conflict of interest

The authors claim that they have no conflicts of interest.

Human/animal rights statement

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Information ScienceHebei UniversityBaodingChina
  2. 2.Intelligent Computing and Financial Security Laboratory, School of Management Engineering and BusinessHebei University of EngineeringHandanChina

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