The need for automated decision-making is steadily increasing. The goal is to derive methods and data-driven 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 computer science.
Our research group Data-Driven Decision Support focuses on automated decision-making in the areas of Revenue Management and Enterprise Systems using quantitative methods of operations research (cf. modelling, simulation, and optimization) and data science (cf. AI/ML). Our research has been published in over 50 renowned OR Journals (EJOR, JEDC, IJPE, IJPR, COR, DGAA, JRPM), distinguished data science conferences (KDD, IJCAI, RECSYS, SDM), and leading computer science venues (VLDB, ICDE, EDBT, DAPD, CIKM, SSDBM). Rainer Schlosser serves as a reviewer for over 40 Journals in the area of management science and data science.
Keywords: Optimal Control of Dynamic Systems, Resource Allocation, Risk-Sensitive & Robust Decision-Making, Causal Inference, Deep Reinforcement Learning, Dynamic Pricing, Inventory Management, ReCommerce Markets, Sustainability, Circular Economy