Generalized cross-entropy based group decision making with unknown expert and attribute weights under interval-valued intuitionistic fuzzy environment

Qi, Xin; Liang, Changyong; Zhang, Junling

doi:10.1016/j.cie.2014.10.017

Cited by 127 publications

(65 citation statements)

References 66 publications

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“…The management of incomplete information has been studied by many researchers [45,46], and lots of methods have been developed for the determination of criteria weights with incomplete information, such as those based on technique for order preference by similarity to an ideal solution (TOPSIS) [19], distance measure [47] and entropy method [48]. In the QFD literature, however, little research has been conducted to estimate the weights of CRs when the weight information is incompletely known.…”

Section: Determine the Importance Weights Of Crsmentioning

confidence: 99%

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

2016

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Quality function deployment (QFD) is a widely used quality system tool for translating customer requirements (CRs) into the engineering design requirements (DRs) of products or services. The conventional QFD analysis, however, has been criticized as having some limitations such as in the assessment of relationships between CRs and DRs, the determination of CR weights and the prioritization of DRs. This paper aims to develop a new hybrid group decision-making model based on hesitant 2-tuple linguistic term sets and an extended QUALIFLEX (qualitative flexible multiple criteria method) approach for handling QFD problems with incomplete weight information. First, hesitant linguistic term sets are combined with interval 2-tuple linguistic variables to express various uncertainties in the assessment information of QFD team members. Borrowing the idea of grey relational analysis (GRA), a multiple objective optimization model is constructed to determine the relative weights of CRs. Then, an extended QUALIFLEX approach with an inclusion comparison method is suggested to determine the ranking of the DRs identified in QFD. Finally, an analysis of a market segment selection problem is conducted to demonstrate and validate the proposed QFD approach.

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Section: Determine the Importance Weights Of Crsmentioning

confidence: 99%

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

2016

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“…On the other hand, also due to the problem complexity or time pressure, criteria weights often cannot be determined with empirical values appropriately in advance [11,64]. Consequently, for the FMCGDM problem of ERSE under discussion, the criteria weighting vector ω and decision makers' weighting vector λ are treated as unknown and to be determined.…”

Section: Problem Formulation Of Erse With Interval-valued Dual Hesitamentioning

confidence: 99%

“…To further analytically compare with widely-used aggregation-operators-based approaches [11,29], we here extend the MCDM approach with IVDHF preferences by Ju, et al [27] to group setting and propose the following Algorithm III, which employs the IVDHFWA operator in Definition 2.7 for information aggregation. Decision making procedures of Algorithm III are also shown in Figure 3.…”

Section: Advantages Of Proposed Approachesmentioning

confidence: 99%

“…It also requires final decisions to balance decision makers' different opinions which are often uncertain and cannot be expressed with crisp values due to problem complexity [8]. As a result, the problems of evaluating emergency response plans for sustainable community development can be categorized as a type of multi-criteria group decision making (MCGDM) [9][10][11][12] problems which involve decision uncertainties caused by evolutions of emergency scenarios [13]. To our best knowledge, only few researches that have been conducted on ERSE under uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Thus far, only Ju, et al [27] and Zhang, et al [28] studied aggregation-operators-based models for multi-criteria decision making (MCDM) under interval-valued dual hesitant fuzzy (IVDHF) environments. Although their approaches can be extended to group settings, aggregation-operators-based models normally at least need to aggregate information twice [11,29], i.e., aggregate individual decision matrices into collective decision matrix, and then aggregate which to obtain final scores. Information loss increases with multiple use of aggregation operators [30], which also raises computational complexity especially for processing hesitant fuzzy preferences [31].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Emergency Response Solutions for Sustainable Community Development by Using Fuzzy Multi-Criteria Group Decision Making Approaches: IVDHF-TOPSIS and IVDHF-VIKOR

et al. 2016

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Emergency management is vital in implementing sustainable community development, for which community planning must include emergency response solutions to potential natural and manmade hazards. To help maintain such solution repository, we investigate effective fuzzy multi-criteria group decision making (FMCGDM) approaches for the complex problems of evaluating alternative emergency response solutions, where weights for decision makers and criteria are unknown due to problem complexity. We employ interval-valued dual hesitant fuzzy (IVDHF) set to address decision hesitancy more effectively. Based on IVDHF assessments, we develop a deviation maximizing model to compute criteria weights and another compatibility maximizing model to calculate weights for decision makers. Then, two ideal-solution-based FMCGDM approaches are proposed: (i) by introducing a synthesized IVDHF group decision matrix into TOPSIS, we develop an IVDHF-TOPSIS approach for fuzzy group settings; (ii) when emphasizing both maximum group utility and minimum individual regret, we extend VIKOR to develop an IVDHF-VIKOR approach, where the derived decision makers' weights are utilized to obtain group decision matrix and the determined criteria weights are integrated to reflect the relative importance of distances from the compromised ideal solution. Compared with aggregation-operators-based approach, IVDHF-TOPSIS and IVDHF-VIKOR can alleviate information loss and computational complexity. Numerical examples have validated the effectiveness of the proposed approaches.

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A variable weight‐based hybrid approach for multi‐attribute group decision making under interval‐valued intuitionistic fuzzy sets

Wei²,

et al. 2020

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Generalized cross-entropy based group decision making with unknown expert and attribute weights under interval-valued intuitionistic fuzzy environment

Cited by 127 publications

References 66 publications

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

Evaluating Emergency Response Solutions for Sustainable Community Development by Using Fuzzy Multi-Criteria Group Decision Making Approaches: IVDHF-TOPSIS and IVDHF-VIKOR

A variable weight‐based hybrid approach for multi‐attribute group decision making under interval‐valued intuitionistic fuzzy sets

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