Achim Koberstein is an Assistant Professor at Johann Wolfgang Goethe University, Frankfurt am Main, where he holds the Detlef Hübner Endowed Chair for Business Administration, specializing in Logistics and Supply Chain Management within the Department of Economics. His research focuses on optimization methodologies applied to industrial and logistical challenges. Research Focus Professor Koberstein's work spans three primary domains: Optimization algorithms and solver technology, production and supply network planning systems, and gas market optimization frameworks. His research integrates mathematical modeling with practical applications in: Advanced algorithm development for large-scale linear programming Strategic planning in automotive manufacturing systems Risk-optimized procurement in liberalized energy markets Sustainable logistics network design Publication Analysis Koberstein's recent publications (2006-2010) demonstrate consistent focus on optimization techniques with industrial applications. His work evolves from foundational algorithm improvements (dual simplex enhancements, Gomory cut strengthening) to domain-specific implementations in automotive production planning, gas procurement, and logistics networks. A strong emphasis emerges on uncertainty modeling, resource optimization, and large-scale system design.
Eric Grosse is a Junior Professor for Business Administration, especially Digital Transformation in Operations Management, at Saarland University, Faculty of Humanities and Social Sciences. His research focuses on integrating human factors—such as ergonomics, fatigue, learning, and behavior—into decision models for logistics and production systems. He leads several interdisciplinary research projects including Human-Centric Production and Logistics, Models for an Active Aging Workforce, and Behavioral Order Picking. University: Saarland University School: Faculty of Humanities and Social Sciences Department: Junior Professorship for Business Administration, especially Digital Transformation in Operations Management Academic Rank: Assistant Professor Email: eric.grosse@uni-saarland.de His research interests include Operations Management, Digital Transformation, Human Factors in Logistics, Ergonomics in Warehousing, Behavioral Operations, and Sustainable Logistics. His work emphasizes human-centered design in warehousing and production, aiming to balance economic efficiency with worker well-being. He develops optimization models that simultaneously minimize costs and physical strain, and explores how learning, incentives, and fatigue affect order picking performance. The analysis of his recent publications reveals a consistent focus on integrating ergonomic and economic objectives in manual materials handling and order picking. Key themes include storage assignment optimization, workforce scheduling with ergonomic constraints, human-robot collaboration, and the impact of worker behavior and fatigue on system performance. His methodologies often combine mathematical modeling, simulation, and empirical validation through case studies and experiments. Eric Grosse actively collaborates with researchers from institutions such as TU Darmstadt and has published in top-tier journals like the International Journal of Production Research, Computers & Industrial Engineering, and International Journal of Operations & Production Management. His work supports warehouse managers in improving working conditions while maintaining operational efficiency. Human-Centric Production and Logistics System Design Models and Methods for an Active Aging Workforce Behavioral Order Picking Green Warehousing Supply Chain Resilience Warehousing 4.0/5.0 Digital Transformation Coaching He advises and co-supervises research with a team of collaborators and has contributed to the development of assessment tools like the Warehouse Error Prevention (WEP) tool. His research is funded by the German Federal Ministry for Economic Affairs and Energy, indicating active grant involvement. Though no formal students are listed, his co-authored papers suggest mentorship of junior researchers. He has no listed scientific awards in the provided text.
Dr. Sander Borst is a postdoctoral researcher in the Algorithms and Complexity group at the Max Planck Institute for Informatics in Saarbrücken, Germany. Previously, he completed his Ph.D. in the Networks & Optimization group at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, advised by Daniel Dadush. He holds bachelor’s degrees in Mathematics and Computer Science and a master’s degree in Mathematics from Delft University of Technology . Current Role: Postdoctoral Researcher at Max Planck Institute for Informatics. Education: B.Sc. and M.Sc. in Mathematics and Computer Science from Delft University of Technology. Sander's research focuses on the design and analysis of online algorithms , particularly in online network design , hypergraph matching , and graph exploration . His work bridges theoretical computer science with practical applications in optimization and algorithmic randomness. Recent projects include developing algorithms for explorable heap selection , constraint propagation in MIP solvers, and analyzing integrality gaps in integer programming under random data assumptions. The trends in his publications (2020–2025) span online optimization , randomized algorithms , hypergraph theory , and integer programming . Key venues include SODA, IPCO, ITCS, and Algorithmica, with recurring themes in network design , combinatorial optimization , and theoretical guarantees for algorithmic solutions. His technical expertise extends to software development and programming languages , ensuring practical implementation of theoretical models. He has collaborated with researchers such as Daniel Dadush, Neil Olver, and Ambros Gleixner.
Ralf Borndörfer is a Professor and Head of the Network Optimization Department at the Zuse Institute Berlin (ZIB) , a leading research institution in mathematical algorithmic intelligence. His work focuses on optimizing complex transportation systems, particularly in railway operations, public transit, and air cargo logistics. He leads projects like Timetabling with Duality and Zonotopes, Symmetric Line Planning, and WILSON-LEARN, which address challenges in train scheduling, electric vehicle integration, and predictive maintenance. Key Research Areas : Mathematical optimization, railway timetabling, public transport planning, game theory for toll enforcement, and electric vehicle scheduling. Notable Collaborations : Projects with Deutsche Bahn, BIFOLD, and MATH+ Cluster of Excellence. His recent publications (2023-2025) explore: Non-linear battery modeling in electric bus scheduling Predictive maintenance integration in rolling stock rotations Logic-constrained shortest paths for flight planning Price-sensitive routing in public transport He has contributed to algorithmic frameworks like the Restricted Modulo Network Simplex Method and Bayesian rolling horizon approaches, emphasizing computational efficiency and real-world applicability.
Prof. Dr. David Bommes is a leading researcher in computer graphics and geometry processing, currently a Professor at the University of Bern . His expertise lies in mesh generation, particularly quadrilateral and hexahedral meshing, numerical optimization, and automatic differentiation techniques. His research focuses on developing robust algorithms for generating high-quality meshes from complex geometries, with applications in CAD, architecture, and simulation. He has made significant contributions to the fields of surface and volume parametrization, directional field synthesis, and geometry processing optimization. Prof. Bommes has received notable recognition, including the Best Paper Award (1st place) at SGP 2022 and the Graphics Replicability Stamp for his work on TinyAD, a lightweight automatic differentiation library for geometry processing. His publications span top-tier venues such as SIGGRAPH, Eurographics, and ACM Transactions on Graphics, covering topics from automatic differentiation and geodesic computation to advanced meshing techniques. He actively collaborates with leading institutions and researchers worldwide.
Aaron Praktiknjo is a University Professor and Chair holder for Energy Systems Economics at RWTH Aachen University, leading the FCN-ESE group within the E.ON Energy Research Center. He contributes to energy economics research with a focus on system integration, market dynamics, and sustainability. Research Areas: Energy system analysis Energy market regulation Security of supply Sustainability assessment Behavioral energy economics Energy transition effects Selected Article Trends: Recent work explores hydrogen supply scenarios, grid infrastructure sustainability, and techno-economic modeling for energy systems. His publications emphasize renewable integration, market volatility, and cross-border energy dynamics. Leadership Roles: Co-Director of E.ON Energy Research Center Chair of Gesellschaft für Energiewissenschaft und Energiepolitik (GEE) Vice-President of International Association for Energy Economics (IAEE) Editorial Board member of Scientific Data (Nature Portfolio) Advising: Currently advising external doctoral candidates Elisabeth Wendlinger and Veronika Engwerth. Collaborates with researchers across FCN-ESE on topics like DC/AC grid comparisons and HVDC integration. Team & Collaborations: Works with technical staff including Christina Kockel, Jakob Kulawik, and Karl Seeger. His group engages in international conferences (e.g., IAEE, IET AC/DC) and interdisciplinary workshops (RWTH EME Doctoral Workshop).
Igor S. Litvinchev is a researcher in the fields of Operations Research , Optimization , and Computational Geometry . His work focuses on mathematical modeling, algorithm design, and applications in industrial engineering, particularly packing and placement problems under geometric constraints. Research Interests: Sphere/ellipse packing, convex hull optimization, Lagrangian heuristics, and applications in additive manufacturing and wireless networks. Recent Collaborations: Co-authoring with Tatiana E. Romanova, Luis Infante, and Aleksandr V. Pankratov on packing problems and facility location models. Key Publications: 2024 studies on parabolic containers , quasi-containment , and soft polygon packing , alongside 2023-2020 works on 3D clusters, circular layouts, and R&D portfolio optimization. Editorial Contributions: 2022 editorial on digitization in organizations, reflecting his interest in real-time systems and academic collaboration.
Dr. Birgit Schwartz-Reinken is a Lecturer at the University of Hamburg's Business School, affiliated with the Institute of Business Information Systems. She holds a Ph.D. in parallel computing and business planning from 1994. Her research focuses on Operations Research, Parallel Algorithms, Supply Chain Management, and E-Learning. She has authored or co-authored 8 publications since 1990, including works on Eclipse-based GUI design and adaptive virtual learning environments. Her work bridges computational optimization and business informatics. Education: Ph.D. in Parallel Processing and Business Planning (1994) Research Interests: Combines parallel algorithm design with business optimization challenges, particularly in supply chain and production planning. Explores E-Learning methodologies for adaptive instructional systems. Publications: Recent work emphasizes software tools like Eclipse for enterprise development, while earlier research addressed parallel computing applications in combinatorial optimization and manufacturing systems. Grants/Advising: No specific grants or advisee records noted in provided texts.
Prof. Dr. Michael Rath is Professor for Building Energy Technology at Hochschule Bochum since February 2022, affiliated with the Department of Civil and Environmental Engineering. He serves as Deputy Chairman of the Committee for Renewable Energy Systems, member of the Faculty Council, and member of several interdisciplinary institutes including the Interdisciplinary Institute for Applied AI and Data Science Ruhr (AKIS), DigiTeach Institute, and the Foundation Institute for the Energy Transition Institute (EnWI). His educational background includes parallel Diplom studies in Physics and Mathematics at Westfälische Wilhelms-Universität Münster (2004-2010), followed by doctoral research at Philipps-Universität Marburg (2010-2015) where he completed his dissertation on turbulence models. Prof. Rath's research spans building energy technology with a strong focus on renewable energy integration, particularly geothermal and solar systems. His work addresses climate-neutral energy systems through innovative approaches to district heating networks, thermal storage solutions, and machine learning applications for energy optimization. He investigates urban heat transition strategies, building automation, and sector coupling to achieve sustainable energy solutions for both new and existing buildings. His publication portfolio reveals consistent research output focused on practical energy solutions, with recent work emphasizing geothermal heat pump optimization, machine learning applications in energy systems, and district heating network design. The research demonstrates a clear trajectory toward integrated, climate-neutral urban energy systems with particular attention to forecasting methods, market-oriented operation, and thermal storage solutions. Prof. Rath actively supervises numerous bachelor's and master's theses, with recent students working on topics including thermal network modeling, sustainable hot water supply, summer heat protection measures, and photovoltaic integration effects on electricity prices. He also serves as second supervisor for doctoral candidates working on decarbonized district heating systems and hierarchical control strategies. He leads the Competence Center for Integrated Building Energy Technology at Fraunhofer IEG and is involved in significant research projects including EnOB: ARCHE (focusing on self-optimizing control systems for distributed energy systems) and the NRW Heat Study by LANUV, where he contributes to regional heat planning with a focus on developing cost frameworks for renovation measures.
Sasanka Potluri serves as Professor of General Computer Science and Medical Informatics at Karlshochschule (Karlsruhe University of Education) since September 2025. He is actively engaged in teaching and research within the Department of Computer Science and Medical Informatics, focusing on the intersection of artificial intelligence and healthcare applications. His academic leadership spans multiple research projects aimed at transforming healthcare delivery through technological innovation. His educational background includes: Dr.-Ing. in Electrical Engineering and Information Technology from Otto-von-Guericke University Magdeburg (Germany) Dipl.-Ing. in Information Technology from Alpen-Adria University Klagenfurt (Austria) B. Tech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, Kakinada (India) Professor Potluri's research spans the cutting edge of artificial intelligence applications in healthcare, with particular expertise in machine learning, deep learning, and generative AI. His work bridges technical innovation with practical healthcare solutions, focusing on clinical decision support systems, biomedical statistics, and digital signal processing. He has developed novel approaches for healthcare logistics optimization, synthetic health data generation, and addressing digital health equity issues. His research methodology combines theoretical rigor with practical implementation, often working at the intersection of computer science, medical informatics, and systems engineering. His publication record reveals a consistent trajectory from industrial control systems security toward healthcare applications of AI. Early work focused on intrusion detection in industrial control systems using deep learning techniques, while recent publications demonstrate a strategic shift toward healthcare logistics, patient transportation optimization, and blood product management. This evolution reflects both his technical expertise in AI and his commitment to addressing critical challenges in healthcare delivery systems. His research increasingly incorporates generative AI approaches to solve complex healthcare resource allocation problems. Professional service includes: Member and Reviewer at GMDS (German Society for Medical Informatics, Biometry and Epidemiology) since 2024 Reviewer for European Federation for Medical Informatics since 2024 Reviewer for IEEE Transactions on Network and Service Management since 2020 Reviewer for Elsevier Journals including Engineering Applications of Artificial Intelligence since 2017 Professor Potluri actively supervises B.Sc, M.Sc, and PhD students in medical informatics, AI applications, generative AI, clinical decision support systems, and healthcare logistics. His current research projects focus on hospital resource and process optimization, synthetic health data generation, digital health equity studies, and generative AI in healthcare. He previously held research positions as Junior Research Group Leader at University Hospital Jena, Project Leader at Otto von Guericke University Magdeburg, and Research Assistant for EU Projects, building a strong foundation for his current interdisciplinary work.
Christian Kirches is a full professor at the Institute for Mathematical Optimization within the Carl-Friedrich-Gauß-Fakultät (Faculty of Mathematics, Technische Universität Braunschweig). His research focuses on nonlinear optimization , mixed-integer optimal control , and robust optimization for dynamic systems. He was awarded the Klaus-Tschira prize (2011) for public science communication and the Hengstberger prize (2014) for junior researchers, and received an ERC Consolidator Grant (2022) for his work on optimization under uncertainty. Alumni of Heidelberg University (Diploma, Doctorate, Habilitation) Former resident associate at Argonne National Laboratory and postdoctoral appointee at the University of Chicago Leader of a junior research group (2013–2017) at Heidelberg University His recent publications highlight advancements in mixed-integer nonlinear programming , real-time control systems , and optimization for sustainable energy and transportation . He collaborates with researchers on projects like wind farm control, hydrogen aviation networks, and chromatography process optimization. His methodological work on sum-up rounding , trust-region algorithms , and combinatorial integral approximation has been published in journals such as SIAM Journal on Optimization, Mathematical Programming, and IEEE Control Systems Letters. Kirches also serves as area coordinator for Optimization Online and was associate editor for OR Spectrum (2022–2024). Scientific Awards: Klaus-Tschira Prize (2011) Hengstberger Prize (2014) ERC Consolidator Grant (2022) He is an elected member of the COIN-OR initiative and contributes to open-source optimization software. His lab at TU Braunschweig develops algorithms for dynamic systems under uncertainty, with applications in energy management, autonomous traffic, and industrial processes.
Prof. Dr. Stefan Weltge is a Professor of Discrete Mathematics at the Department of Mathematics, Technical University of Munich (TUM). He holds a PhD in Mathematics from Otto von Guericke University Magdeburg (2016) and was a postdoctoral researcher at ETH Zurich. His research focuses on combinatorial optimization, integer programming, and polyhedral combinatorics, with notable contributions to extension complexity and mixed-integer programming. He has received multiple teaching awards at TUM, including the TUM Supervisory Award (2022) and Best Lecturer recognitions in 2019 and 2021/22. His work has been published in leading journals such as the Journal of the ACM and Journal of Combinatorial Theory B. Prof. Weltge’s academic contributions include groundbreaking research on the complexity of mixed-integer programs, polyhedral representations, and combinatorial optimization problems. He has organized conferences like OR 2024 and the Cargese Workshops on Combinatorial Optimization, and serves on program committees for IPCO, MIP, and ISCO. His research is supported by DFG grants, including the Individual Grant (NextGen) and the AdONE PhD Program. He advises PhD students working on topics such as integer programming, algorithm design, and optimization theory. His publications span theoretical advancements in convex optimization, linear programming relaxations, and applications in logistics and operations research. Notable articles include work on bounded subdeterminants in integer programs and the complexity of stable set problems. His research bridges discrete mathematics with practical algorithmic solutions, influencing both theoretical and applied domains.
Ambros Gleixner is a Professor at HTW Berlin since 2020 and an affiliated researcher at the Zuse Institute Berlin (ZIB) since 2008. His research focuses on computational aspects of mixed-integer linear and nonlinear programming, with emphasis on exact rational arithmetic and algorithm verification. PhD in Mathematics (2015), Technische Universität Berlin Diplom (MSc) in Mathematics (2008), Technische Universität Berlin Vordiplom (BSc) in Mathematics (2004), Universität Bayreuth His work spans mathematical optimization, operations research, and computational mathematics. At ZIB, he leads projects like developing the MINLP solver SCIP , the LP solver SoPlex , and verifying integer programming results through VIPR . Recent publications highlight advancements in exact rational MIP, GPU-parallel algorithms, and energy system optimization. Scientific Awards : MERIT Visiting Scholar at University of Melbourne (2013) Teaching : Offers bachelor's theses in optimization and computational mathematics. Requires students to have attended relevant seminars and possess programming skills. Office hours by email appointment through Ambros.Gleixner@HTW-Berlin.de . Labs & Teams : Principal investigator at ZIB's Mathematical Algorithmic Intelligence division, Research Campus MODAL , and Linear, Integer, and Constraint Programming project.
Prof. Dr. Christian Almeder serves as Professor and Head of the Chair of Supply Chain Management within the Faculty of Business Administration and Economics at Viadrina European University (Frankfurt (Oder), Germany). His research focuses on operations research applications in production planning, logistics, and supply chain optimization, with particular expertise in lot sizing, scheduling, and perishable goods management. He maintains active research output with publications spanning from 1997 to 2023. Almeder's research centers on mathematical modeling of complex production and logistics systems. His primary contributions involve developing heuristic and metaheuristic solutions for capacitated lot sizing problems, multi-level scheduling, and integrated production-distribution planning. Key specialties include handling perishability constraints, lead time uncertainties, and batch processing requirements using genetic programming, simulation-based optimization, and clearing function approaches. His work bridges theoretical operations research with industrial applications in supply chain management. Analysis of his 15 most recent publications (2013-2023) reveals a consistent focus on lot sizing and scheduling, with increasing emphasis on integrated supply chain problems and perishable goods logistics. Methodologically, he combines metaheuristics (genetic programming, simulated annealing) with simulation techniques to address real-world complexities like stochastic processing times and limited buffers. His work demonstrates strong application in production planning parameter tuning, vehicle routing integration, and robust operational planning under uncertainty.
Marco Lübbecke is a Professor at the Faculty of Business and Economics of RWTH Aachen University , Germany. He serves as the chairholder of the Lehrstuhl für Operations Research and holds the position of Studiendekan (Dean of Studies) for his faculty. His professional contact is marco.luebbecke@rwth-aachen.de, with additional administrative contact at luebbecke@or.rwth-aachen.de. Current academic rank: Professor Faculty: Business and Economics Department: Operations Research Marco Lübbecke's research focuses on Operations Research and Mixed Integer Programming . He develops and analyzes algorithms like Branch-and-Price , Dantzig-Wolfe Reformulation , and Decomposition Methods to solve complex optimization problems in industrial, transportation, and political science applications. Core research areas: Combinatorial Optimization, Mathematical Programming Application domains: Logistics, Rail Transport, Redistricting, Manufacturing Systems Methodological interests: Column Generation, Cutting Planes, Algorithm Engineering His recent publications highlight advancements in optimization software frameworks like the SCIP Optimization Suite and algorithmic techniques for solving large-scale linear and integer programs. Key research trends include automated decomposition methods, structural analysis of MIPs, and hybrid approaches combining classical optimization techniques with machine learning insights. Marco Lübbecke actively participates in academic conferences and serves as a co-author in numerous technical reports and journal publications, contributing to the development of efficient optimization algorithms and their practical implementation.
