Shadowed Sets-Based Linguistic Term Modeling and Its Application in Multi-Attribute Decision-Making

Wang, Huidong; He, Shifan; Pan, Xiaohong; Li, Chengdong

doi:10.3390/sym10120688

Cited by 7 publications

(8 citation statements)

References 27 publications

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“…Yao et al [21] give a new decision-theoretic formulation of shadowed sets according to the principle of uncertainty invariance. In the multi-attribute decision-making problem involving public evaluation, shadowed sets can be directly constructed by a data-driven method from the collected data [22,23].…”

Section: Q-rung Orthopair Fuzzy Set (Q-rofs)mentioning

confidence: 99%

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An Extended VIKOR Method Based on q-Rung Orthopair Shadowed Set and Its Application to Multi-Attribute Decision Making

Wang¹,

Zhang²,

Yao³

2020

Symmetry

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In the multi-attribute decision making (MADM) process, the attribute values are sometimes provided by experts or the public in the form of words. To model the linguistic evaluation more accurately, this paper proposes the q-rung orthopair shadowed set (q-ROSS) to represent attribute values and extends the VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method to solve MADM problems in the q-ROSS context. First, we propose the q-ROSS to express evaluation information. Some basic operation rules and distance measures are investigated accordingly. When the amount of data is large, the left and right endpoints of the collected interval numbers will obey symmetric normal distribution. Secondly, based on the normal distribution assumption, the collected data intervals are mapped to shadowed sets through a data processing approach. Furthermore, we extend the VIKOR model to tackle the MADM problem where the evaluation values are expressed by q-rung orthopair shadowed numbers. A location selection problem verifies the practicability of our method, and the effectiveness and superiority of the presented approach are reflected through comparative analysis.

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Section: Q-rung Orthopair Fuzzy Set (Q-rofs)mentioning

confidence: 99%

“…The result of shadowed sets are shadowed numbers. A shadowed number (SN) is defined with the following parameters [22].…”

Section: Shadowed Setmentioning

confidence: 99%

An Extended VIKOR Method Based on q-Rung Orthopair Shadowed Set and Its Application to Multi-Attribute Decision Making

Wang¹,

Zhang²,

Yao³

2020

Symmetry

Self Cite

View full text Add to dashboard Cite

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“…Fuzzy logic was used by Zhang [29] to assess commercial viability of technology start-up businesses in a government venture capital, while Shen and Tzeng [30] developed a fuzzy inferenceenhanced VC-DRSA model for technical analysis of the investment. Other researches have been conducted in the area of: knowledge management performance measurement of small and medium enterprises [31], social behavior modeling [32], measuring customer loyalty [33], e-commerce [34], MADM (multi-attribute decision-making) [35][36][37][38][39][40][41], material selection [42], risk assessment [43], medical diagnosis [44], mobile robots [45], investment selection [46] and forecasting [47].…”

Section: State Of the Artmentioning

confidence: 99%

A Fuzzy Logic Algorithm for Optimizing the Investment Decisions within Companies

2019

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As companies operate in a competitive environment, where the struggle for survival on the market is rather tough, the top management face new challenges to identify methods, and even techniques, which allows it to select from the market those assets that provide an optimal ratio between the acquisition cost and the economic performance. In this context, a fuzzy logic managerial decision tool for the assets acquisition is proposed with the paper. The algorithm has three main components: the matrix of the membership degree of the existing bids to asset selection criteria, using fuzzy triangular numbers; the vector of the global membership degree of the bids to the selection criteria and the maximum of the global membership degree as an inference operator for establishing the validated bids by the algorithm. Two scenarios of asset acquisition were tested. After simulations, it was determined that the proposed fuzzy logic managerial decision tool combines, with very good results, the acquisition cost of the assets with their economic performance.

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“…Other practical applications including fuzzy logic that can be mentioned are: the design of a warning system and fire monitoring system for smart buildings [15], modeling pedestrian dynamic behavior [16], choosing the location for power plants [17], supplier evaluation and selection [18], investment decision optimization [19], assessing the commercial viability of technology start-up businesses [20], e-commerce regional cooperation [21], material selection procedures [22], risk assessment [23], cooperative mobile robots' learning [24], and alternatives' evaluation [25], etc. As for the theoretical aspects related to fuzzy logic application with regard to multi-attribute decision making, more can be read in [26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Symmetry 2019 11 X For Peer Review 3 Of 18mentioning

confidence: 99%

The Development of a Fuzzy Logic System in a Stochastic Environment with Normal Distribution Variables for Cash Flow Deficit Detection in Corporate Loan Policy

2019

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This paper develops a Mamdani fuzzy logic system (FLS) that has stochastic fuzzy input variables designed to identify cash-flow deficits in bank lending policies. These deficits do not cover the available cash-flow (CFA) resulting from the company's operating activity. Thus, due to these deficits, solutions must be identified to avoid companies' financial difficulties. The novelty of this paper lies in its using stochastic fuzzy variables, or those categories of variables that are defined by fuzzy sets, characterized by normally distributed density functions specific to random variables, and characterized by fuzzy membership functions. The variation intervals of the stochastic fuzzy variables allow identification of the probabilistic risk situations to which the company is exposed during the crediting period using the Mamdani-type fuzzy logic system. The mechanism of implementing the fuzzy logic system is based on two stages. The first is based on the determination of the cash-flow requirements resulting from loan reimbursement and interest rates. This stage has the role of determining the need for financial resources to cover the liabilities. The second stage is based on the identification of the stochastic fuzzy variables which have a role in influencing the cash flow deficits and the probability values estimation of these variables taking into account probability calculations. Based on these probabilistic values, using the Mamdani fuzzy logic system, estimations are computed for the available cash-flow (the output variable). The estimated values for CFA are then used to detect probability risk situations in which the company will not have enough resources to cover its liabilities to financial creditors. All the FLS calculations refer to future time periods. Testing and simulating the fuzzy controller confirms its functionality.

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Shadowed Sets-Based Linguistic Term Modeling and Its Application in Multi-Attribute Decision-Making

Cited by 7 publications

References 27 publications

An Extended VIKOR Method Based on q-Rung Orthopair Shadowed Set and Its Application to Multi-Attribute Decision Making

An Extended VIKOR Method Based on q-Rung Orthopair Shadowed Set and Its Application to Multi-Attribute Decision Making

A Fuzzy Logic Algorithm for Optimizing the Investment Decisions within Companies

The Development of a Fuzzy Logic System in a Stochastic Environment with Normal Distribution Variables for Cash Flow Deficit Detection in Corporate Loan Policy

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