We propose introducing fairness constraints to one of the most famous multi-criteria decision-making methods, the analytic hierarchy process (AHP). We offer a solution that guarantees consistency while respecting legally binding fairness constraints in AHP pairwise comparison matrices. Through a synthetic experiment, we generate the comparison matrices of different sizes and ranges/levels of the initial parameters (i.e., consistency ratio and disparate impact). We optimize disparate impact for various combinations of these initial parameters and observed matrix sizes while respecting an acceptable level of consistency and minimizing deviations of pairwise comparison matrices (or their upper triangles) before and after the optimization. We use a metaheuristic genetic algorithm to set the dually motivating problem and operate a discrete optimization procedure (in connection with Saaty’s 9-point scale). The results confirm the initial hypothesis (with 99.5% validity concerning 2800 optimization runs) that achieving fair ranking while respecting consistency in AHP pairwise comparison matrices (when comparing alternatives regarding given criterium) is possible, thus meeting two challenging goals simultaneously. This research contributes to the initiatives directed toward unbiased decision-making, either automated or algorithm-assisted (which is the case covered by this research).

The article solves the problem of more efficient and economical training of combat crews on short range air defense systems. The training so far is based on the use of conventional means, which require engagement of a large number of people, expensive equipment, long-term planning and spending a lot of time and space. The use of unmanned aerial vehicles - drones, greatly saves all these resources. Our mathematical model, using the methods of multicriteria decision-making - Analytical Hierarchical Processes (AHP) and optimization - The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), determines the weighting coefficients and then ranks alternatives or, the ranking for drone selection, which would replace classical means for training and co- aching so meeting all parameters and characteristics of the training itself and the training equipment. The methods first prioritize the selection criteria, and then, based on their importance, concretize the solution among the offered alternatives.