For more than 10 years we develop various complex intelligent computer systems
We conduct research in many scientific fields like Multi-Criteria Decision Analysis, Fuzzy theory, Risk management, etc.
We work on integration between GIS and Multi-Criteria Decision Analysis systems to deal with environmental and land management tasks
We also provide services of education and training professionals in the field of Multi-Criteria Decision Analysis
"DECERNS (www.decerns.com) is a unique decision support platform that allows for the examination of the same case using multiple MCDA tools...
It also allows for uncertainty analysis through probability distributions and fuzzy numbers associated with model weights and scores."
"DECERNS software is a crucial educational tool that made this book possible."
"Within DECERNS, the effects of using different models (AHP versus MAUT, etc.) can be explored with ease by implementing the model with multiple methods. MAVT, AHP, TOPSIS and PROMETHEE have an option to conduct sensitivity analyses to changes in weights (Yatsalo, Didenko, et al. 2010). MAUT and ProMAA compute functions of random values instead of using Monte Carlo methods to deal with uncertainty; the fuzzy methods are also computed as functions of fuzzy variables."
"As our aim is to describe methods and software packages that require no a priori technical knowledge and can be used by beginners and experienced practitioners alike, we describe the DECERNS project in more detail. The DECERNS project includes several methods that have been described in the previous chapters of this book. It also contains an integrated Geographical Information System that can be useful if the decision maker is dealing with with spatial alternatives."
"MAUT application is supported by software (i.e. DecideIT and DECERNS) with simple and intuitive interfaces to structure the assessment and the sensitivity analysis (Buchholz et al., 2009, Linkov and Moberg, 2012). This is the same for the AHP software, whose structure is straightforward and easily understandable (Fernandez, 1996, Buchholz et al., 2009, Linkov and Moberg, 2012, InfoHarvest, 2014)."