Robust two-stage optimization consensus models with uncertain costs

Abstract

In the consensus-reaching process (CRP), decision-makers (DMs) frequently encounter the dilemma of too much uncertain information, which can lead the actual decision to deviate from the optimal solution obtained by the currently used consensus models. To do this, we construct two robust two-stage optimization consensus models with uncertain costs and obtain their robust two-stage counterparts. We then apply a Benders decomposition algorithm to solve the resulting models. Finally, the experimental results show that the new models are better suited for uncertain contexts and could help DMs produce more reliable choices.