A Novel Linguistic <i>Z</i>-Number QUALIFLEX Method and Its Application to Large Group Emergency Decision Making

Ding, Xuefeng; Zhu, Lixia; Lu, Mei-Shun; Wang, Qi; Feng, Yi-Qi

doi:10.1155/2020/1631869

Cited by 13 publications

(6 citation statements)

References 44 publications

(67 reference statements)

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“…How to deal with uncertain information effectively is an open problem. Several mathematical theories have been discussed for solving this problem, such as Bayesian theory [2], fuzzy set theory [3,4], Dempster-Shafer (D-S) evidence [5,6], possibility theory [7,8], D-numbers [9,10], Z-numbers [11,12], rough set theory [13,14], and fractal theory [15,16]. Especially D-S evidence theory, as a rational and effective method for dealing with uncertain information, has the following three advantages.…”

Section: Introductionmentioning

confidence: 99%

Determination of Evidence Weights Based on Convolutional Neural Network for Classification Problem

Hou

Deng

et al. 2022

Journal of Sensors

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The weighted average is an efficient way to address conflicting evidence combination in the Dempster-Shafer evidence theory. However, it is an open issue how to determine the evidence weights reasonably. Although many traditional conflicting evidence combination solutions based on evidence distance or entropy have been put forward, the evidence weights are determined with a single aspect, and no comprehensive consideration of other useful information affects the weights. Thus, it does not ensure that determination of weights is the most reasonable. By introducing deep learning into conflicting evidence combination, this paper proposes a comprehensive method for determining the evidence weights based on a convolutional neural network. Taking the evidence as the network input and the corresponding weight as the output, it utilizes convolutional neural network to fully mine potentially useful information that affects the evidence weights, in order to determine the weights comprehensively. Additionally, we define a weight loss function. The weights are continuously optimized through back propagation and achieve the optimal when the weight loss function value is the minimum. Classification experimental results demonstrate that the proposed method outperforms traditional ones based on evidence distance or entropy and can be flexibly extended to other application fields as a decision-making fusion method.

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Section: Introductionmentioning

confidence: 99%

Determination of Evidence Weights Based on Convolutional Neural Network for Classification Problem

Hou

Deng

et al. 2022

Journal of Sensors

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“…The probabilistic linguistic weighted averaging operator combining Dempster–Shafer evidence theory was advanced by Li and Wei [ 22 ] for the EDM of a serious mine accident. Ding et al [ 11 ] suggested a large group EDM method by extending the QUALIFLEX method using linguistic Z-numbers. Ashraf and Abdullah [ 2 ] designed three EDM algorithms using TOPSIS, grey relational analysis and Einstein aggregation operators under a spherical fuzzy environment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Different approaches have been adopted to rank the EDM solutions, such as Technique in Order of Preference by Similarity to Ideal Solution (TOPSIS) [ 2 ], VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) [ 60 ], Weighted Distance Based Approximation (WDBA) and COmbinative Distance-based ASsessment (CODAS) [ 46 ], DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) [ 4 ], QUALItative FLEXible multiple criteria (QUALIFLEX) [ 11 ] and Preference Ranking Organization METHod for Enrichment Evaluations II (PROMETHEE II) [ 45 ]. Although these methods are beneficial to EDM, they assume that DMs are completely rational.…”

Section: Introductionmentioning

confidence: 99%

A collaborative emergency decision making approach based on BWM and TODIM under interval 2-tuple linguistic environment

Chai

Duan

et al. 2021

Int. J. Mach. Learn. & Cyber.

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Emergencies require various emergency departments to collaborate to achieve timely and effective emergency responses. Thus, the overall performance of emergency response is influenced not only by the efficiency of each department alternative but also by the coordination effect among different department alternatives. This paper proposes a collaborative emergency decision making (CEDM) approach considering the synergy among different department alternatives based on the best–worst method (BWM) and TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) method within an interval 2-tuple linguistic environment. First, the evaluation information provided by decision makers (DMs) is represented by interval 2-tuple linguistic variables to reflect and model the underlying diversity and uncertainty. On the basis of the DMs’ evaluations, the individual and collaborative performance evaluations of multi-alternative combinations composed of different department alternatives are constructed. Then, the BWM is extended into interval 2-tuple linguistic environment to obtain the weights of evaluation criteria, where the group decision making is taken into account in an interval fuzzy mathematical programming model. Furthermore, to derive more practical and accurate decision results, an interval 2-tuple linguistic TODIM (ITL-TODIM) method is proposed by considering the DMs’ psychological behaviours. In the developed ITL-TODIM method, both the gain and loss degrees of one alternative relative to another are simultaneously computed. Finally, a numerical example is presented to illustrate the applicability of the proposed method. Sensitivity and comparative analyses are also provided to demonstrate the effectiveness and advantages of the proposed approach.

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“…Since then, it has obtained a lot of attentions. Some researchers presented theoretical studies of Z-numbers, like Z*-numbers [9], arithmetic operations of discrete and continuous Z-numbers [10,11], modeling of Z-number [12], approximate reasoning of Z-numbers [13], functions based on a Z-number set [14], total utility of Z-numbers [15] and so on; while other researchers developed some applications of Z-numbers, such as Z-evaluations [16], sensor data fusion using Z-numbers [17], decision making approaches with Z-numbers [18][19][20][21][22][23][24], Z-numbers-based stable strategies analysis in evolutionary game [25], Z-numbers-based medicine selection of the patients with mild symptoms of the COVID-19 [26], Z-numbers-based environmental assessment under uncertainty [27] and so on.…”

Section: Introductionmentioning

confidence: 99%

Some aggregation operators of neutrosophic Z-numbers and their multicriteria decision making method

Rui

et al. 2020

Complex Intell. Syst.

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As the generalization of the classical fuzzy number, the concept of Z-number introduced by Zadeh indicates more ability to depict the human knowledge and judgments of both restraint and reliability as an order pair of fuzzy numbers. In indeterminacy and inconsistent environment, a neutrosophic set is described by the truth, falsity, and indeterminacy degrees, but they lack measures related to reliability. To describe the hybrid information of combining the truth, falsity and indeterminacy degrees with their corresponding reliability degrees, this paper first proposes the concept of a neutrosophic Z-number (NZN) set, which is a new framework of neutrosophic values combined with the neutrosophic measures of reliability, as the generalization of the Z-number and the neutrosophic set. Then, we define the operations of neutrosophic Z-numbers (NZNs) and a score function for ranking NZNs. Next, we present NZN weighted arithmetic averaging (NZNWAA) and NZN weighted geometric averaging (NZNWGA) operators to aggregate NZN information and investigate their properties. Regarding the NZNWAA and NZNWGA operators and the score function, a multicriteria decision making (MDM) approach is developed in the NZN environment. Finally, an illustrative example about the selection problem of business partners is given to demonstrate the applicability and effectiveness of the developed MDM approach in NZN setting.

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A Novel Linguistic Z-Number QUALIFLEX Method and Its Application to Large Group Emergency Decision Making

Cited by 13 publications

References 44 publications

Determination of Evidence Weights Based on Convolutional Neural Network for Classification Problem

Determination of Evidence Weights Based on Convolutional Neural Network for Classification Problem

A collaborative emergency decision making approach based on BWM and TODIM under interval 2-tuple linguistic environment

Some aggregation operators of neutrosophic Z-numbers and their multicriteria decision making method

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