Much of existing decision making research focuses on algorithms designed tomeasure the preferences of different experts on a finite set of alternatives withregard to multiple conflicting criteria and trade them off alongside objectiveproblem-relevant data. Moreover, a number of analytical methods have beendeveloped to handle informational gaps and uncertainty. However, practicaldecision structures and problem requirements are versatile and more complexthan the existing single-method approaches assume. Therefore, the issue ofconstructing user-friendly decision support procedures able to capturecomplexity and produce valid solutions is a relevant and unresolved problem.This thesis addresses the challenges of developing system-oriented decisionsupport models and methods for complex multi-criteria group decisionproblems involving uncertainty. In essence, this research builds upon multicriteriadecision analysis (MCDA) theory and system analytical concepts. Firstof all, the limitations and advantages of different MCDA techniques areanalyzed and new integrated methodologies are developed to benefit from thestrengths of individual methods while avoiding their weaknesses. Furthermore,this thesis studies the existing empirical and analytical approaches to groupdecision making and proposes novel methods to aggregate the opinions ofdifferent experts. In particular, the focus is on the aspects of group structuringand responsibilities definition, as well as on measuring and analyzingsubjective expert estimates reliability and discordance of opinions in groups.Finally, the developed methods are capable of considering informationaluncertainties by using the tools of fuzzy set theory and its generalizations.Additionally, various methods from the broader science of operations researchand management as well as visualization techniques are employed to preciselymodel the practical systems.