scholarly journals Representing Uncertainty in Group Decision Making Through the Hesitant Information Set Approach

2021 ◽  
Author(s):  
Manish Agarwal
Keyword(s):  
Decision Making ◽  
Soft Computing ◽  
Group Decision Making ◽  
Real World ◽  
Group Decision ◽  
Original Form ◽  
Different Types ◽  
Information Set ◽  
Interval Valued

Abstract In multi attribute group decision making (MAGDM), different types of un- certainties co-exist on account of hesitancy, vagueness, and incompleteness besides the diversity in evaluations. To cater to this problem of represent- ing such uncertain evaluations in their original form, new datastructures have been proposed. In this regard, the concept of hesitant information set (HIS) [Applied Soft Computing, 2018] is of great significance. To add to its usefulness in the representation of vague evaluations, it is extended to the continuous and interval-valued domains. A number of illustrative examples are included to show their worth in the real world group decision-making. The basic operations for them, along with their properties, are investigated. A real case-study is included.

scholarly journals Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 223 ◽  
Author(s):  
Chao Zhang ◽  
Deyu Li ◽  
Xiangping Kang ◽  
Yudong Liang ◽  
Said Broumi ◽  
...  
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Granular Computing ◽  
Probabilistic Models ◽  
Group Decision ◽  
Real Life ◽  
Theoretical Models ◽  
Problem Solving Strategies ◽  
Interval Valued

In plenty of realistic situations, multi-attribute group decision-making (MAGDM) is ubiquitous and significant in daily activities of individuals and organizations. Among diverse tools for coping with MAGDM, granular computing-based approaches constitute a series of viable and efficient theories by means of multi-view problem solving strategies. In this paper, in order to handle MAGDM issues with interval-valued neutrosophic (IN) information, we adopt one of the granular computing (GrC)-based approaches, known as multigranulation probabilistic models, to address IN MAGDM problems. More specifically, after revisiting the related fundamental knowledge, three types of IN multigranulation probabilistic models are designed at first. Then, some key properties of the developed theoretical models are explored. Afterwards, a MAGDM algorithm for merger and acquisition target selections (M&A TSs) with IN information is summed up. Finally, a real-life case study together with several detailed discussions is investigated to present the validity of the developed models.

scholarly journals Interval-Valued Probabilistic Hesitant Fuzzy Set Based Muirhead Mean for Multi-Attribute Group Decision-Making

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 342 ◽  
Author(s):  
Krishankumar ◽  
Ravichandran ◽  
Ahmed ◽  
Kar ◽  
Peng
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Fuzzy Set ◽  
Group Decision ◽  
Partial Information ◽  
Programming Model ◽  
Hesitant Fuzzy Set ◽  
Occurrence Probability ◽  
Source Selection ◽  
Interval Valued

As a powerful generalization to fuzzy set, hesitant fuzzy set (HFS) was introduced, which provided multiple possible membership values to be associated with a specific instance. But HFS did not consider occurrence probability values, and to circumvent the issue, probabilistic HFS (PHFS) was introduced, which associates an occurrence probability value with each hesitant fuzzy element (HFE). Providing such a precise probability value is an open challenge and as a generalization to PHFS, interval-valued PHFS (IVPHFS) was proposed. IVPHFS provided flexibility to decision makers (DMs) by associating a range of values as an occurrence probability for each HFE. To enrich the usefulness of IVPHFS in multi-attribute group decision-making (MAGDM), in this paper, we extend the Muirhead mean (MM) operator to IVPHFS for aggregating preferences. The MM operator is a generalized operator that can effectively capture the interrelationship between multiple attributes. Some properties of the proposed operator are also discussed. Then, a new programming model is proposed for calculating the weights of attributes using DMs’ partial information. Later, a systematic procedure is presented for MAGDM with the proposed operator and the practical use of the operator is demonstrated by using a renewable energy source selection problem. Finally, the strengths and weaknesses of the proposal are discussed in comparison with other methods.

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