The need for automated decision-making is steadily increasing. The goal is to derive methods and models for automated decision support for practical applications in uncertain and changing environments. Solving problems in practical applications requires bringing together data management, analytics, optimization, and risk management.
Our research group Data-Driven Decision Support focuses on data-driven decision-making in the areas of Revenue Management and Enterprise Systems using quantitative methods of operations research (modelling, simulation, and optimization) and data science (cf. AI, ML, and RL). Our research has been published in renowned OR Journals (EJOR, JEDC, COR, IJPE, IJPR, DGAA, JRPM), distinguished data science conferences (KDD, IJCAI, RECSYS, SDM), and leading computer science venues (VLDB, ICDE, EDBT, DAPD, CIKM, SSDBM).
Keywords: Optimal Control of Dynamic Systems, Resource Allocation, Risk-Sensitive & Robust Decision-Making, Causal Inference, Dynamic Pricing, Inventory Management, ReCommerce Markets, Sustainability, Circular Economy