The eigenvectors for each row were calculated using geometric

principles (multiplying the value for each criterion in each column in the same

row of the original pair-wise comparison matrix and then applying this to each

row) as follows:

Egi=

(3.2)

Where, Egi = eigenvalue for the row i; n = number of elements

in row i. The priority vector is determined by normalizing the eigenvalue to

1(divided by their sum) as follows:

(3.3)

The lambda max (?max) was obtained from the summation of

products between each element of priority vector and the sum of columns of the

reciprocal matrix as shown in the following formula:

(3.4)

Where, aij = the sum of criteria in each column in the

matrix; Wi = the value of weight for each criterion

which is corresponding to the priority vector in the matrix

of decision, where the values (i=1, 2,…., m) and (j= 1, 2,…., n). So, the

lambda max (?max) in this study is equal to 16.852.

The CI (consistency index) was estimated using the following

Eq. (3.5):

CI

= (?max ? n)/(n ? 1) (3.5)

Where,

CI represents the equivalent to the mean deviation of each comparison element

and the standard deviation of the evaluation error from the true ones (Sólnes,

2003), , and n is size or order of the matrix.

In

this study, CI = 0.1323.

The consistency ratio (CR) was obtained according to Saaty,

1980, by dividing the value of consistency index (CI) by the Random index

value (RI = 1.59) for n=15 (Table 3.4), where this table displays mean Random

index value RI for matrices with different sizes according to (Saaty, 1980).

CR = (CI/RI) (3.6)

If CR less than 0.1, the ratio indicates a reasonable consistency

level in the pairwise comparison. CR should, therefore, be less than 0.1.

In this study, CR = 0.0832