Multi-Criteria Decision Analysis (MCDA) problems very often involve multiple Decision Makers (DMs). In this paper, we present several new decision analysis algorithms to consider both subjective decision criteria and objective decision criteria with different treatment of their uncertainty. The uncertainty and availability of weight for decision criteria are also studied, and probability scoring is developed to score the criteria. Finally, a case study is presented.
Keywords: multi-criteria decision analysis, Bayesian, scoring, uncertainty