Consensus Decision Modeling with Distributionally Robust Chance Constraint in Uncertain Environments

Group decision-making (GDM) in an ambiguous environment has consistently become a research focus the decision science field during past decade. Existing minimum cost consensus models either control total budget deterministic context or on improving utility of makers. In this study, novel model with distributionally robust chance constraint (DRO-MCCM) is explored. First, two constraints are developed based varied preferences decision-makers and taking into consideration uncertainty unit adjustment cost. Next, construct conditional value-at-risk (CVaR) to approximate constraint, simulate viewpoint makers such as function Gaussian distribution, convert feasible semidefinite programming problem using dual theory moment method. Finally, supply chain management scenario involving new product prices employs these models. Comparison sensitivity analyses demonstrates model’s superiority effectiveness.