dealing with complex decision making, and may aid the decision maker to set priorities and make
the best decision. By reducing complex decisions to a series of pairwise comparisons, and then
synthesizing the results, the AHP helps to capture both subjective and objective aspects of a
decision. In addition, the AHP incorporates a useful technique for checking the consistency of the
decision maker’s evaluations, thus reducing the bias in the decision making process.

The AHP considers a set of evaluation criteria, and a set of alternative options among which the
best decision is to be made. It is important to note that, since some of the criteria could be
contrasting, it is not true in general that the best option is the one which optimizes each single
criterion, rather the one which achieves the most suitable tradeoff among the different criteria.
The AHP generates a weight for each evaluation criterion according to the decision maker’s
pairwise comparisons of the criteria. The higher the weight, the more important the corresponding
criterion. Next, for a fixed criterion, the AHP assigns a score to each option according to the
decision maker’s pairwise comparisons of the options based on that criterion. The higher the score,
the better the performance of the option with respect to the considered criterion. Finally, the AHP
combines the criteria weights and the options scores, thus determining a global score for each
option, and a consequent ranking. The global score for a given option is a weighted sum of the
scores it obtained with respect to all the criteria.
2 Features of the AHP
3 Implementation of the AHP
The AHP can be implemented in three simple consecutive steps:
1) Computing the vector of criteria weights
2) Computing the matrix of option scores.
3) Ranking the options.

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