Deriving priorities from fuzzy pairwise comparison judgements

Mikhailov, Ludmil

doi:10.1016/s0165-0114(02)00383-4

Cited by 531 publications

(300 citation statements)

References 18 publications

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“…It takes the constraints of priority weights as fuzzy constraints and constructs the membership functions of fuzzy constraints to express DM's satisfaction. By maximizing DM's satisfaction degree, the fuzzy program is established to determine the priority weights from IVMPR (Mikhailov 2002(Mikhailov , 2003(Mikhailov , 2004Chen and Xu 2015).…”

Section: Shortcomings Of the Fpm For Deriving The Priority Weights Frmentioning

confidence: 99%

“…The above membership function is often employed in the FPM to derive the priority weights from an IVMPR (Mikhailov 2002(Mikhailov , 2003(Mikhailov , 2004Chen and Xu 2015). However, there exist some shortcomings when using Eq.…”

Section: Letmentioning

confidence: 99%

“…(b) Many studies (Mikhailov 2002(Mikhailov , 2003(Mikhailov , 2004Chen and Xu 2015) applied the convex probability distribution in probabilistic methods to fuzzy/multiplicative preference relation. Especially, the triangular or trapezoidal fuzzy judgments are usually employed in the FPMs (Mikhailov 2002(Mikhailov , 2003(Mikhailov , 2004Chen and Xu 2015).…”

Section: Approaches To Solving the Constructed Intuitionistic Fuzzy Pmentioning

confidence: 99%

“…Especially, the triangular or trapezoidal fuzzy judgments are usually employed in the FPMs (Mikhailov 2002(Mikhailov , 2003(Mikhailov , 2004Chen and Xu 2015). Furthermore, Hauser and Tadukamalla (1996) proved that the symmetric triangular distribution can provide more consistent and reasonable ranking.…”

Section: Approaches To Solving the Constructed Intuitionistic Fuzzy Pmentioning

confidence: 99%

“…He constructed a fuzzy programming model to derive priority weights from an IVMPR and thus designed a fuzzy analytical approach to solving partnership selection problems. Mikhailov (2003) transformed the interval prioritization task into a fuzzy linear programming problem for deriving optimal crisp priorities from an IVMPR. By transforming interval judgments into fuzzy inequality constraints, Mikhailov (2004) established linear or non-linear membership functions to represent DMs' satisfaction degree.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An intuitionistic fuzzy programming method for group decision making with interval-valued fuzzy preference relations

Wan

Wang

et al. 2016

Fuzzy Optim Decis Making

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Section: Shortcomings Of the Fpm For Deriving The Priority Weights Frmentioning

confidence: 99%

Section: Letmentioning

confidence: 99%

Section: Approaches To Solving the Constructed Intuitionistic Fuzzy Pmentioning

confidence: 99%

Section: Approaches To Solving the Constructed Intuitionistic Fuzzy Pmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An intuitionistic fuzzy programming method for group decision making with interval-valued fuzzy preference relations

Wan

Wang

et al. 2016

Fuzzy Optim Decis Making

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Data‐enabled cognitive modeling: Validating student engineers’ fuzzy design‐based decision‐making in a virtual design problem

Irgens

Chesler

Linderoth

et al. 2017

Comp Applic In Engineering

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The ability of future engineering professionals to solve complex real-world problems depends on their design education and training. Because engineers engage with open-ended problems in which there are unknown parameters and multiple competing objectives, they engage in fuzzy decision-making, a method of making decisions that takes into account inherent imprecisions and uncertainties in the real world. In the design-based decision-making field, few studies have applied fuzzy decision-making models to actual decision-making process data. Thus, in this study, we use datasets on student decision-making processes to validate approximate fuzzy models of student decision-making, which we call data-enabled cognitive modeling. The results of this study (1) show that simulated design problems provide rich datasets that enable analysis of student design decision-making and (2) validate models of student design cognition that can inform future design curricula and help educators understand how students think about design problems.

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The revised extent analysis method

Arman

Hadi-Vencheh

2021

Concurrency and Computation

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The degree of possibility is used by the extent analysis (EA) to extract the crisp local weights from a triangular fuzzy comparison matrix (FCM); while, it is just a relation for comparing two triangular fuzzy numbers. This causes EA to extract the inaccurate local weights from a FCM and sometimes to rank the alternatives incorrectly. In this study, we revise the EA to avoid this misapplication. For this purpose, we propose a procedure in which the global weights of alternatives are obtained as fuzzy values; then, the degree of possibility is used just for ranking the alternatives based on their fuzzy global weights. The advantages of the revised extent analysis (REA), proposed in this study, compared with other fuzzy analytic hierarchy process are its simplicity and low computations. This study gives three numerical examples to illustrate how REA avoids the flaws of EA.

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Deriving priorities from fuzzy pairwise comparison judgements

Cited by 531 publications

References 18 publications

An intuitionistic fuzzy programming method for group decision making with interval-valued fuzzy preference relations

An intuitionistic fuzzy programming method for group decision making with interval-valued fuzzy preference relations

Data‐enabled cognitive modeling: Validating student engineers’ fuzzy design‐based decision‐making in a virtual design problem

The revised extent analysis method

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