Big Data Analysis in Multi-Criteria Choice Problems

The problem of multi-criteria choice with non-uniform scales of criteria is considered. A model of a multicriteria choice problem is described, the main elements of which are sets of alternatives and quality criteria, as well as algorithms that allow ranking alternatives without prior reduction of the criteria scales to homogeneous ones. Algorithms for constructing aggregated ranking of alternatives are based on the construction of preference matrices by criteria containing information on the degree of superiority of one alternative over another. Propositions are proved that allow ranking alternatives with assessments according to two quality criteria. Algorithms for indexing alternatives are proposed that allow ranking alternatives for an arbitrary number of criteria. The best aggregated ranking is determined by the total distance to the rankings of alternatives by criteria. All algorithms have polynomial computational complexity, which makes it possible to work with large arrays of initial information. A software system for ranking alternatives in problems with big data has been developed. The initial information is stored in Excel tables, which makes it easy to take into account the limitations on the criteria scales. The operation of the software system is demonstrated by the example of choosing the best version of a drone for purchase in order to observe the terrain, shoot it and transmit information to the operator.

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