Outranking Decision-Making Method with Z-Number Cognitive Information

Peng, Hong-gang; Wang, Jianqiang

doi:10.1007/s12559-018-9556-y

Cited by 35 publications

(14 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Definition 1 (discrete fuzzy number) [4,26]. A fuzzy subset A of the real line R with membership function μ A : R ⟶ [0, 1] is a discrete fuzzy number if its support is finite, i.e., there exist…”

Section: Preliminariesmentioning

confidence: 99%

“…is section redefines a more detailed Z-number outranking relation for the outranking relation in article [26]. Combining the three aspects of geometry, algebra, and information entropy with Z-number and the hidden probability information of Z-number itself, three different computing methods of dominance degree are defined.…”

Section: Outranking Relationship and Dominancementioning

confidence: 99%

“…e dominance degree of Z j and Z k is measured from three perspectives of geometry, algebra, and information entropy, respectively. Compared with the degree of superiority defined in article [26], the three measurements in this paper consider part A and part B of Z-number together. It is unreasonable to calculate part B separately in the literature.…”

Section: Outranking Relationship and Dominancementioning

confidence: 99%

“…Peng and Wang [26] defined a kind of outranking relationship between two Z-numbers, but it directly calculates the dominance degree of the first element A and the second element B in a Z-number by a simple comparison and accumulation, which makes no sense in some practical problems. For example, suppose that Z 1 � (high, uncertainly) and Z 2 � (low, absolutely); then, the "high" and "low" can be directly compared.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dominance Degree Multiple Attribute Decision Making Based on Z‐Number Cognitive Information

Yao

Mao

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Cognitive information can be described by Z-number fully and effectively. However, many problems of Z-number need to be further studied. In this paper, two hidden probability models for calculating Z-number are established to provide more intuitive and abundant information. Next, the dominance degree relationship of Z-number is developed and subdivided. Furthermore, combined with the hidden probability of calculation, three different measurements of dominance degree are defined from three levels of geometry, algebra, and cross entropy based on the outranking relationship. The influencing factors are analyzed for different combinations of two probability models and three dominance degree measures. A multiattribute decision model is established on the basis of new grey association analysis and QUALIFLEX method. Finally, a decision example is given to verify the effectiveness and feasibility of the method. And sensitivity analysis is made to determine the impact of parameters and hidden probability on the decision model.

show abstract

Section: Preliminariesmentioning

confidence: 99%

Section: Outranking Relationship and Dominancementioning

confidence: 99%

Section: Outranking Relationship and Dominancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dominance Degree Multiple Attribute Decision Making Based on Z‐Number Cognitive Information

Yao

Mao

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In recent years, in order to effectively solve MCDM problems from different angles, there is an increasing number of studies concerning the MCDM methods, such as Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method [11], Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method [38,39], an acronym in Portuguese for interactive and multi-criteria decision-making (TODIM) method [56], preference ranking organization method for enrichment evaluations (PROMETHEE) method, elimination and choice expressing reality (ELECTRE) [25,26], and qualitative flexible multiple criteria method (QUALIFLEX) [14,32]. QUALIFLEX method aims to obtain the permutation whose ranking best reflects the preorders according to the pair-wise comparisons of all alternatives [36].…”

mentioning

confidence: 99%

Picture fuzzy multi-criteria group decision-making method to hotel building energy efficiency retrofit project selection

Zhang

Wang

et al. 2020

RAIRO-Oper. Res.

Self Cite

View full text Add to dashboard Cite

Building energy consumption accounts for a considerable proportion on energy consumption. To reduce building energy consumption, building energy efficiency retrofitting (BEER) based on Energy Performance Contracting mechanism is the most feasible and cost-effective method. With the increase number of BEER projects, BEER project selection has become an essential problem for energy service companies. In this paper, a multi-criteria group decision-making (MCGDM) method is proposed to deal with BEER project selection problem. First, picture fuzzy sets are employed to describe the evaluation information under the complex and uncertain environment. Subsequently, picture fuzzy weighted average operator and Laplace distribution-picture fuzzy order weighted average operator are proposed based on convex combination to aggregate individual evaluations into the overall evaluations. Furthermore, picture fuzzy TOPSIS-based QUALIFLEX method is developed to identify the optimal ranking of alternatives. Moreover, the practicality, effectiveness and advantages of the proposed MCGDM method are illustrated using a case study of hotel BEER project selection and comparative analysis. Finally, conclusions about primary contributions, and future discussions of the proposed method are demonstrated.Mathematics Subject Classification. 62C86.

show abstract

Novel multi-attribute decision-making method based on Z-number grey relational degree

et al. 2022

View full text Add to dashboard Cite

Outranking Decision-Making Method with Z-Number Cognitive Information

Cited by 35 publications

References 56 publications

Dominance Degree Multiple Attribute Decision Making Based on Z‐Number Cognitive Information

Dominance Degree Multiple Attribute Decision Making Based on Z‐Number Cognitive Information

Picture fuzzy multi-criteria group decision-making method to hotel building energy efficiency retrofit project selection

Novel multi-attribute decision-making method based on Z-number grey relational degree

Contact Info

Product

Resources

About