Fuzzy multi-criteria decision analysis (FMCDA) is based on assessing functions of fuzzy arguments and ranking of fuzzy numbers. In the general case, implementing each of the above operations requires using the appropriate computer modules. All the current FMCDA systems are based on approximate estimates of the functions of fuzzy arguments. The purpose of this paper is to create and apply the FMCDA system, which implements all the main approaches to evaluating functions of fuzzy numbers as well as different methods for ranking of fuzzy numbers by a fuzzy extension of the classical MCDA method TOPSIS as an example.

The paper presents the functional capabilities of the developed Decerns-FT computer system and its features, including the usability of fuzzy TOPSIS (FTOPSIS) models of various levels of complexity, depending on the chosen method of evaluating functions of fuzzy arguments and the method for ranking fuzzy numbers; it describes the general structure of the system and its major blocks. In this paper, the example of Decerns-FT implementation is presented to analyze distinctions in ranking alternatives within MCDA problems by different FTOPSIS models with the use of approximate methods for estimating functions of fuzzy numbers, standard fuzzy arithmetic, and a reduced and general transformation methods. For this, the Monte Carlo module is used to generate numerous scenarios for multi-criteria problems. Using the Decerns-FT system, it is shown for the first time that distinctions in the ranking alternatives by FTOPSIS models, which differ in approaches to estimating functions of fuzzy numbers and ranking methods, are significant.

The developed computer system Decerns-FT has no analogs in the class of systems that implement FMCDA models. The modules of the Decerns-FT system form the basis for the development of other FMCDA systems, which are components of the DecernsFMCDA decision support system, designed to solve a wide range of scientific and applied problems of multi-criteria decision analysis in conditions of uncertainty/fuzziness, and also for the use within the relevant university courses and training of specialists.