Weight Determination of Audit Criteria under Uncertainty Using the VIKOR Method and Optimized by Genetic Algorithm and Simulation

Auditors frequently leverage their professional judgment when assigning importance to evaluation criteria in system audits. In this paper we investigate the application of the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for the determination of optimal criteria weights within the audit process. To address the inherent probability and uncertainty associated with weight assignment, we have employed Monte Carlo simulation (MCS) and Genetic Algorithm (GA). The VIKOR method, recognized for its efficacy in multi-criteria decision analysis, is adapted to systematically aggregate expert opinions and preferences. By integrating MCS, we generate a diverse set of plausible weight vectors, thereby acknowledging the subjective element in auditor judgment. The results from the numerical example demonstrate the practicality and robustness of the proposed approach. The VIKOR method, combined with MCS, effectively captures the variability and uncertainty inherent in assigning criteria weights. Additionally, the GA offers a complementary perspective, enabling auditors to compare solutions derived from different optimization techniques. The comparative analysis reveals that both methods yield consistent and reliable weight assignments, reinforcing the credibility of the proposed framework. The entire model has been developed using MATLAB software.

Keywords:

VIKOR , Monte Carlo simulation , genetic algorithm , Auditors