Determining decision makers’ weights in group ranking: a granular computing method

Wang, Baoli; Liang, Jiye; Qian, Yuhua

doi:10.1007/s13042-014-0278-5

Cited by 42 publications

(13 citation statements)

References 41 publications

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“…This section provides the proposed programming model for calculating DMs' weight values. Most often, DMs' weight values are directly provided as input, which causes inaccuracies in the decision-making process [28]. As argued by Koksalmis and Kabak [22], the calculation of DMs' weight values is substantial for rational decision-making, and it prevents inaccuracies in the decision-making process.…”

Section: Proposed Programming Model For Dms' Weight Calculationmentioning

confidence: 99%

Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information

et al. 2019

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As an attractive generalization of the intuitionistic fuzzy set (IFS), q-rung orthopair fuzzy set (q-ROFS) provides the decision makers (DMs) with a wide window for preference elicitation. Previous studies on q-ROFS indicate that there is an urge for a decision framework which can make use of the available information in a proper manner for making rational decisions. Motivated by the superiority of q-ROFS, in this paper, a new decision framework is proposed, which provides scientific methods for multi-attribute group decision-making (MAGDM). Initially, a programming model is developed for calculating weights of attributes with the help of partially known information. Later, another programming model is developed for determining the weights of DMs with the help of partially known information. Preferences from different DMs are aggregated rationally by using the weights of DMs and extending generalized Maclaurin symmetric mean (GMSM) operator to q-ROFS, which can properly capture the interrelationship among attributes. Further, complex proportional assessment (COPRAS) method is extended to q-ROFS for prioritization of objects by using attributes' weight vector and aggregated preference matrix. The applicability of the proposed framework is demonstrated by using a renewable energy source prioritization problem from an Indian perspective. Finally, the superiorities and weaknesses of the framework are discussed in comparison with state-of-the-art methods.Sustainability 2019, 11, 4202 2 of 21 inferred from their analysis that India has a high scope for renewable energy sources and it can effectively manage energy crisis by proper planning and management. Recently, Mardani et al. [4] conducted a detailed analysis on the use of multi-attribute group decision-making (MAGDM) methods for solving energy management problems, and it can be inferred from the analysis that energy source evaluation and selection can be effectively solved by using MAGDM perspectives. Furthermore, there is uncertainty in the process of selection, which can be effectively managed by using fuzzy sets and its variants [5]. Baek and Lee et al. [6] proposed a new design strategy for optimal selection of renewable energy system (RES) in buildings. Gonzalez et al. [7] presented a conceptual model to understand the relationship among different factors that correspond to the sustainability and acceptance of RES projects. Cavallaro et al. [8,9] presented decision frameworks under intuitionistic fuzzy context to rationally select solar-hybrid power plants.Motivated by these claims, in this paper, we propose a new decision framework for rational prioritization of renewable energy sources. The preference information used here is q-rung orthopair fuzzy set (q-ROFS) [10], which is a powerful generalization of the intuitionistic fuzzy set (IFS) [11] and Pythagorean fuzzy set [12]. q-ROFS provides a wider window to decision makers (DMs) for preference elicitation by relaxing the constraint (sum of the degree of membership and non-membership less than or equal to...

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Section: Proposed Programming Model For Dms' Weight Calculationmentioning

confidence: 99%

Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information

et al. 2019

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“…In many other situations the definition of the weights is controversial, because there are no indisputable criteria that can be used for this operation. Weights are often imposed by decision-makers, according to political strategies (see Wang et al [42]). For example, the scientific committee of a competitive examination for promotion of Faculty members may decide that scientific publications will account for 30% of the total performance, the international projects for 25%, the teaching activity for 35%, etc.…”

Section: Assigning a Numerical Weight To Each Expertmentioning

confidence: 99%

Decision-making in semi-democratic contexts

Franceschini

García-Lapresta

2019

Information Fusion

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A general problem, which may concern practical contexts of different nature, is to aggregate multi-experts rankings on a set of alternatives into a single fused ranking. Aggregation should also take into account the experts' importance, which may not necessarily be the same for all of them. We synthetically define this context as semi-democratic. The main aim of the paper is the analysis of the possible semi-democratic paradigms that can be conceived when the experts' importance is not the same: (i) the importance is described by means of a weighting vector; (ii) the importance is expressed by a weak order on the set of experts; (iii) the importance is described by a weak order on the set of experts with additional information on the ordinal proximities among them. The three paradigms can be applied in different decision-making situations, where some experts perform multiple assignments. In this paper various situations are discussed and analyzed in detail. A series of examples, in the field of interior design of a new car, will complement the description.

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“… A set of m decision-making agents 1 (D 1 , D 2 , …, D m ) expressing their opinion on the alternatives, through preference orderings (e.g., a > [(b ~ c) || d] > e >…, where symbols ">", "~" and "||" respectively mean "strictly preferred to", "indifferent to" and "incomparable to");  An importance hierarchy of the agents, which is expressed through a linear rank-ordering (e.g., D 1 > D 2 > (D 3 ~ D 4 ) > …) and not through a set of weights, as in most of the decision-making problems (Martel and Ben Khelifa, 2000;Vora et al, 2014;Wang et al, 2014);  A consensus 2 ordering of the alternatives, which represents the solution of the problem. Franceschini et al (2015) classified this specific problem as ordinal semi-democratic; the adjective semi-democratic indicates that agents do not necessarily have the same importance, while ordinal indicates that their rank is defined by a linear ordering (Nederpelt and Kamareddine, 2004).…”

Section: );mentioning

confidence: 99%

Checking the consistency of the solution in ordinal semi-democratic decision-making problems

Franceschini

Maisano

2015

Omega

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An interesting decision-making problem is that of aggregating multi-agent preference orderings into a consensus ordering, in the case the agents' importance is expressed in the form of a rankordering. Due to the specificity of the problem, the scientific literature encompasses a relatively small number of aggregation techniques. For the aggregation to be effective, it is important that the consensus ordering well reflects the input data, i.e., the agents' preference orderings and importance rank-ordering. The aim of this paper is introducing a new quantitative tool-represented by the so-called p indicators-which allows to check the degree of consistency between consensus ordering and input data, from several perspectives. This tool is independent from the aggregation technique in use and applicable to a wide variety of practical contexts, e.g., problems in which preference orderings include omissions and/or incomparabilities between some alternatives. Also, the p indicators are simple, intuitive and practical for comparing the results obtained from different techniques. The description is supported by various application examples.

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Determining decision makers’ weights in group ranking: a granular computing method

Cited by 42 publications

References 41 publications

Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information

Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information

Decision-making in semi-democratic contexts

Checking the consistency of the solution in ordinal semi-democratic decision-making problems

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