Tierney, KevinKevinTierneyBalzereit, KajaKajaBalzereitBunte, AndreasAndreasBunteNiehörster, OliverOliverNiehörster2022-11-172022-11-172022https://publica.fraunhofer.de/handle/publica/42885910.1109/etfa52439.2022.9921490Decision support systems have become a critical component in the planning processes of companies needing to solve difficult optimization problems. Multi-stage, stochastic optimization problems pose a particular challenge for decision makers, as the uncertainty in the input data makes it hard to determine the correct decisions. The scalable stochastic optimization (SSO) technique proposes a way of solving these problems, but is not able to provide feedback to a decision maker regarding why it makes its decisions. We suggest a mechanism for explaining the feedback of SSO to help decision makers better understand a decision support system’s recommendations.endecision supportstochastic optimizationexplainabilityconference paper