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The surveyed data was classified using the Analysis Hierarchy Process (AHP) method.
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This paper employs the analysis hierarchy process (AHP) to determine the weights of indexes for factors.
To help resolve this issue, we conducted a multi-criteria decision analysis using the Analytic Hierarchy Process.
This was done by questioning stakeholder-representatives in a multi-criteria analysis session, using the analytic hierarchy process (AHP) method of Saaty [8], and applied earlier [3].
Resources are allocated to tasks based on task weight calculated using analysis hierarchy process.
(3) A multilevel analysis using the hierarchy outlined above.
For that, we performed a statistical analysis using the multinomial-Poisson hierarchy model (Zeng et al. 2010) to take into account variations in sample size and sampling errors.
To classify the deficiencies as enhancer, suppressor, or having no effect, we took into account the variation in X NDJ observed in the control genotype and then performed a statistical analysis using the multinomial-Poisson hierarchy model (Zeng et al. 2010) with a 95% confidence interval.
Our model has some similarities to Tran et al. (2002) in that they developed a method to rank watersheds using fuzzy indicators and landscape variables although they structured their analysis into a decision hierarchy using the Analytical Hierarchy Process (AHP).
Using the method of hierarchies analysis the weight of the alternatives priorities are defined, allowing for the estimation of the degree of influence of design features of passenger cars bodies on the considered criteria and for the selection of reasonably efficient constructive scheme for domestic passenger car body.
The importance weights of the other six selection criteria are ascertained using the fuzzy analytic hierarchy process (AHP) analysis.
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