The DecernsFMCDA fuzzy multi-criteria decision support system
Journal: Software & Systems (Vol.35, No. 2)Publication Date: 2022-06-16
Authors : S.V. Gritsyuk; A.V. Korobov; A.V. Radaev; B.I. Yatsalo;
Page : 171-183
Keywords : decernsfmcda system; decerns project; fuzzy number; fuzzy set; management of risk; uncertainty analysis; multi-criteria decision analysis; decision support system;
Abstract
Risk management in the field of environmental protection, remediation of contaminated sites and land use planning requires using modern decision support systems. This paper presents a DecernsFMCDA fuzzy decision support system, which includes both well-known ordinary multicriteria decision analysis methods and original methods for dealing with uncertainties based on fuzzy sets and probabilistic approaches. There is an overview of the available computer systems for multi-criteria decision analysis, as well as a detailed description of the structure of DecernsFMCDA, its main components and differences from other multi-criteria analysis systems. The paper includes the list of classical, probabilistic and original fuzzy models of multicriteria decision analysis implemented as part of the system, as well as diagrams and descriptions of the general modular architecture of DecernsFMCDA and the original libraries of multicriteria decision analysis (mcda-lib4) and a library for working with fuzzy numbers (fuzzylib). A practical application of the DecernsFMCDA system is shown on the case of the multicriteria problem of finding the optimal method for producing single-wall carbon nanotubes. The problem analysis involves the original fuzzy models FTOPSIS and FMAVT implemented within the framework of the system. The DecernsFMCDA fuzzy decision support system is currently the only system that actu-ally implements all the main methods for solving discrete MADM problems, including dealing with un-certainties. The system allows forming and exploring scenarios using various models of multicriteria decision analysis, including those with different sets of parameters of specified models, for subsequent comparison and analysis of the output results as a part of the decision support process.
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