A multiple attribute group decision making method based on two novel intuitionistic multiplicative distance measures

Liao, Huchang; Zhang, Cheng; Luo, Li

doi:10.1016/j.ins.2018.05.023

Cited by 37 publications

(17 citation statements)

References 34 publications

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“…In this case, we make use of the projection‐based distance formula to handle this question. Compared with the traditional distance measures, the projection‐based distance measures can depict the deviation between q ‐ROFNs more clearly . With the help of projection‐based distance measure and TOPSIS, we can deduce the decision rules of three‐way decisions.…”

Section: Basic Model Of Q‐rofdtrssmentioning

confidence: 99%

“…If the value of

normalP normalr normalo j_{β} α

is bigger, it means that the degree of

α

approaching

β

is higher. According to the result of Liao et al, the projection of q ‐ROFNs has the following property:…”

Section: Basic Model Of Q‐rofdtrssmentioning

confidence: 99%

“…According to the result of Liao et al, the projection‐based distance of q ‐ROFNs has the following property:…”

Section: Basic Model Of Q‐rofdtrssmentioning

confidence: 99%

“…Meanwhile, we introduce the TOPSIS method to deduce decision rules of three‐way decisions. To measure the distances between the object and the ideal points of TOPSIS, we utilize the project‐based distance measures, which can reflect the distance between two points more accurately by considering their distances and angles . In many real‐world situations, we often encounter the group decision‐making (GDM) mode because of time pressure and lack of knowledge or uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…To measure the distances between the object and the ideal points of TOPSIS, we utilize the project-based distance measures, which can reflect the distance between two points more accurately by considering their distances and angles. 42,43 In many real-world situations, we often encounter the group decision-making (GDM) mode because of time pressure and lack of knowledge or uncertainty. GDM can evaluate decision-making problems by several experts and then aggregate all these evaluation results to make a suitable choice.…”

Section: Introductionmentioning

confidence: 99%

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q‐Rung orthopair fuzzy sets‐based decision‐theoretic rough sets for three‐way decisions under group decision making

Liang

Cao

2019

Int J Intell Syst

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As an extension of Pythagorean fuzzy sets, the q‐rung orthopair fuzzy sets (q‐ROFSs) can easily solve uncertain information in a broader perspective. Considering the fine property of q‐ROFSs, we introduce q‐ROFSs into decision‐theoretic rough sets (DTRSs) and use it to portray the loss function. According to the Bayesian decision procedure, we further construct a basic model of q‐rung orthopair fuzzy decision‐theoretic rough sets (q‐ROFDTRSs) under the q‐rung orthopair fuzzy environment. At the same time, we design the corresponding method for the deduction of three‐way decisions by utilizing projection‐based distance measures and TOPSIS. Then, we extend q‐ROFDTRSs to adapt the group decision‐making (GDM) scenario. To fuse different experts’ evaluation results, we propose some new aggregation operators of q‐ROFSs by utilizing power average (PA) and power geometric (PG) operators, that is, q‐rung orthopair fuzzy power average, q‐rung orthopair fuzzy power weighted average (q‐ROFPWA), q‐rung orthopair fuzzy power geometric, and q‐rung orthopair fuzzy power weighted geometric (q‐ROFPWG). In addition, with the aid of q‐ROFPWA and q‐ROFPWG, we investigate three‐way decisions with q‐ROFDTRSs under the GDM situation. Finally, we give the example of a rural e‐commence GDM problem to illustrate the application of our proposed method and verify our results by conducting two comparative experiments.

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Section: Basic Model Of Q‐rofdtrssmentioning

confidence: 99%

“…If the value of