A Novel MAGDM Approach With Proportional Hesitant Fuzzy Sets

Xiong, Shenghua; Chen, Zhen‐Song; Chin, Kwai‐Sang

doi:10.2991/ijcis.11.1.20

Cited by 22 publications

(28 citation statements)

References 44 publications

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“…Unlike other developments, this scheme is indeed a unified mechanism and is easily applicable to many real life decision-making problems without enlarging the computational burden. Motivated by [14,22], in the following, we design the TOPSIS in the PDHFS regard. This decision raking procedure involves the following steps.…”

Section: Topsismentioning

confidence: 99%

“…In what follows, the detailed comparative analysis is presented with the traditional approach and Ref. [22] approach to illustrate the essentiality of our proposed scheme.…”

Section: Comparative Analysis Of the Proposed Decision-making Schemementioning

confidence: 99%

“…Assume that DMs provide their decision information in the form of proportional hesitant fuzzy sets rather than proportional dual hesitant fuzzy elements. Then, according to proportional hesitant fuzzy TOPSIS method [22], we solve the fuzzy MAGDM as follows:…”

Section: Handling the Case Study Through The Proportional Hesitant Fumentioning

confidence: 99%

“…This repetition is important and removal of this may lead to unfruitful decision results. Recently, Xiong et al [22] defined a proportional hesitant fuzzy set (PHFS) to handle deficiency 2 of HFS. This is a new approach for handling the imprecise data with more care.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Multi-Attribute Group Decision-Making Approach in the Framework of Proportional Dual Hesitant Fuzzy Sets

Bashir

Rashid

et al. 2019

Applied Sciences

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Making decisions are very common in the modern socio-economic environments. However, with the increasing complexity of the social, today’s decision makers (DMs) face such problems in which they hesitate and irresolute to provide their views. To cope with these uncertainties, many generalizations of fuzzy sets are designed, among them dual hesitant fuzzy set (DHFS) is quite resourceful and efficient in solving problems of a more vague nature. In this article, a novel concept called proportional dual hesitant fuzzy set (PDHFS) is proposed to further improve DHFS. The PDHFS is a flexible tool composed of some possible membership values and some possible non-membership values along with their associated proportions. In the theme of PDHFS, the proportions of membership values and non-membership values are considered to be independent. Some basic operations, properties, distance measure and comparison method are studied for the proposed set. Thereafter, a novel approach based on PDHFSs is developed to solve problems for multi-attribute group decision-making (MAGDM) in a fuzzy situation. It is totally different from the traditional approach. Finally, a practical example is given in order to elaborate the proposed method for the selection of the best alternative and detailed comparative analysis is given in order to validate the practicality.

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Section: Topsismentioning

confidence: 99%

“…In what follows, the detailed comparative analysis is presented with the traditional approach and Ref. [22] approach to illustrate the essentiality of our proposed scheme.…”

Section: Comparative Analysis Of the Proposed Decision-making Schemementioning

confidence: 99%

Section: Handling the Case Study Through The Proportional Hesitant Fumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Multi-Attribute Group Decision-Making Approach in the Framework of Proportional Dual Hesitant Fuzzy Sets

Bashir

Rashid

et al. 2019

Applied Sciences

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“…Typical multi-attribute decision-making (MADM) structures often involve the following three components: (1) evaluation of various alternatives against certain attributes, (2) determination of the attributes weights reflecting the preference of a decision maker, and (3) aggregation of the attributes evaluations taking account of the attributes weights [1]. A considerable number of MADM methodologies and theories have been developed and applied in a wide range of fields, such as bid evaluation, enterprise strategy planning, quality assessment, and product recommendation [2][3][4][5][6][7]. However, in many decision-making scenarios, decision makers do not always follow the above procedures in a stepwise manner, since they usually have some rough idea in the first place of the appropriate outputs for some prototype inputs [8,9].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Interrelationships among Attributes in Multi-Attribute Decision-Making: An Empirical Analysis

Chen¹,

Zhang²,

Rodríguez³

et al. 2019

IJCIS

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Tremendous effort has been exerted over the past few decades to construct multi-attribute decision functions with the capacity to model heterogeneous interrelationships among attributes. In this paper, we report an empirical study aiming to test whether or not considering interrelationships among attributes can benefit the representation of real preferences in multi-attribute ranking tasks. The generalized extended Bonferroni mean (GEBM) has recently been advocated as a promising and efficient tool for modeling heterogeneous interrelationships among attributes. We compare the GEBM with one of its most widely adopted competitors, simple additive weighting (SAW), in terms of their fitting quality when applied to preference elicitation. The attribute performances are manifested uniformly with the use of three widely-adopted utility measurements. Subsequently afterwards, the maximum split approach to establish the constraint objective function in regression for both the GEBM and the SAW to test whether or not all constraints resulting from the subject's ranking can be fulfilled. On this bases, the number of fully or partly fitted subjects, consistency for subjects according to the better fitting model, and reliability of attribute weights learned by either the GEBM or the SAW are empirically examined in a bid to demonstrate the quantitative construction of fitting quality measurement. With the established fitting quality measurement, the necessity of taking heterogeneous interrelationships among attributes into account when constructing multi-attribute decision functions to represent real preferences can be analyzed. The main conclusion from the empirical study suggests that the relative performance of the two aggregation paradigms examined here depends on which fitting quality measurements are adopted. Researchers enthusiastic to discover the heterogeneous interrelationships among attributes when constructing multi-attribute decision functions might find the present results relevant when modeling actual preferences, and consequently this work should serve as a useful reference for enterprises and service providers seeking to strategically drive customer purchasing decisions.

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Power‐average‐operator‐based hybrid multiattribute online product recommendation model for consumer decision‐making

et al. 2021

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This study develops a power‐average‐operator‐based hybrid multiattribute online product recommendation model that considers the consumer's risk attitude to rank categoric product options as a complement to existing recommender systems. Online production recommendation plays a key role in the development of e‐commerce, and can greatly improve consumers' shopping experiences. However, few online shopping sites provide interactive decision aids for consumers such that they can articulate their preferences towards multiple selection attributes with the purpose of mitigating choice difficulty and improving decision quality. Additionally, consumers' risk attitudes to online shopping dramatically impact their product choices. In the model proposed in this paper, the risk attitude‐based power average (RAPA) operator is used to integrate the risk attitude of the decision‐maker into the information fusion process of multiple attribute decision‐making. Subsequently, the risk attitude function, with several basic types, is introduced to quantify the risk attitude of the decision‐maker for use in the RAPA operator. A proportional hesitant fuzzy 2‐tuple linguistic term set (PHF2TLTS) is constructed by incorporating a binary of linguistic information aiming to comprehensively analyze the hybrid product information. With a focus on the information fusion process, the proportional hesitant 2‐tuple linguistic RAPA operator and weighted proportional hesitant 2‐tuple linguistic RAPA operator are introduced to aggregate a given set of PHF2TLTSs. The validity of the proposed model is demonstrated using an illustrative example, a comparison with existing approaches and detailed explanations of the performance differences.

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A Novel MAGDM Approach With Proportional Hesitant Fuzzy Sets

Cited by 22 publications

References 44 publications

A Novel Multi-Attribute Group Decision-Making Approach in the Framework of Proportional Dual Hesitant Fuzzy Sets

A Novel Multi-Attribute Group Decision-Making Approach in the Framework of Proportional Dual Hesitant Fuzzy Sets

Heterogeneous Interrelationships among Attributes in Multi-Attribute Decision-Making: An Empirical Analysis

Power‐average‐operator‐based hybrid multiattribute online product recommendation model for consumer decision‐making

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