**4. Results**

This part illustrates the implementation of the above algorithms for checking the stability with several examples. For clarity, the algorithms for checking the stability of the AHP method will use the same pairwise comparison matrices. In the first case, to assess the stability of the methods, a 6x6 matrix with a good consistency index is taken, CI = 0.025, RI = 1.25, CR = 0.02 < 1. The weights of the criteria of the first matrix are 0.0873, 0.4246, 0.149, 0.2585, 0.0496, and 0.0311 (Table 1).


**Table 1.** The first pairwise comparison matrix (6 × 6).

In the second case, to assess the stability of the first and second algorithms, a 6 × 6 matrix with a critical consistency index is taken, CI = 0.116, RI = 1.25, CR = 0.09 < 1 (Table 2). When the data of such a matrix change, even a small percentage of deviation can change the consistency of the data. The weights of the criteria of the second matrix are 0.3601, 0.1544, 0.2804, 0.0712, 0.0433, and 0.0905 (Table 2).

**Table 2.** The second pairwise comparison matrix (6 × 6).


In all the above algorithms for checking stability, the value of the largest relative error *δ <sup>ξ</sup>* of the values of the criteria weights is taken. The stability of the method is established using different numbers of iterations: 100, 10,000, and 100,000. As with a small number of iterations the values of the largest relative errors *δ ξ* change with each new check, the check is carried out several times (ten attempts). From the values obtained from the ten attempts, an interval is established, that is, the smallest and the largest values of the largest relative errors *δ* (*ξ*) *j* for each of the criteria calculated by formula (9).
