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 beyond using quantitative methods of operations research (cf. modelling, simulation, and optimization) and data science (cf. AI/ML). Our research has been published in over 70 peer-reviewed publications including renowned OR Journals (EJOR, JEDC, IJPE, IJPR, COR, DGAA, JRPM, JIMS, JCLP), distinguished data science conferences (KDD, IJCAI, RECSYS, SDM, ICDE), and leading computer science venues (VLDB, EDBT, DAPD, CIKM, EDOC, SSDBM). Rainer serves as a reviewer for over 50 Journals in the areas of operations management, data science, and information systems.
Keywords: Revenue Management and Pricing, Business Analytics, Risk-Sensitive & Robust Decision-Making, Markov Decision Processes, Deep Reinforcement Learning, Optimal Control, Inventory Management, Resource Allocation, ReCommerce Markets, Sustainability, Circular Economy