Adaptive Relaxations for Multistage Robust Optimization

Multistage robust optimization problems can be interpreted as two-person zero-sum games between two players. We exploit this game-like nature and utilize a game tree search in order to solve quantified integer programs (QIPs). In algorithmic environment relaxations are repeatedly called asses the quality of branching variable for generation bounds. A useful relaxation, however, must well balanced with regard its computing time. present that incorporate scenarios from uncertainty set, whereby considered set is continuously adapted according latest information gathered during process. Using selection, assignment, runway scheduling testbed, we show impact our findings.