جستجوی استاد راهنمای دانشگاهی

René M.B.M. de Kosterمشاهده پروفایل

استاد

• Logistics
• Operations Management
• Warehousing
• +۷ مورد دیگر

René M.B.M. de Koster is a Full Professor of Logistics and Operations Management at the Rotterdam School of Management (RSM), Erasmus University, where he has been a faculty member since 1995. He holds a PhD from Eindhoven University of Technology (1988) and is a leading expert in warehousing, material handling, and sustainable logistics. PhD, Eindhoven University of Technology, 1988 Professor, RSM, Erasmus University, 1995–present Honorary Francqui Chair, Hasselt University, 2018 His research focuses on warehousing systems , robotics in logistics , container terminals , and behavioural operations . He integrates operations research with real-world logistics challenges, emphasizing sustainability and automation. His work contributes to UN Sustainable Development Goals related to responsible consumption and industry innovation. The most recent publications highlight a strong trend toward autonomous systems and AI-driven logistics , particularly in robotic fulfillment, dynamic routing, and human-robot collaboration. These works reflect interdisciplinary engagement with computer science, industrial engineering, and behavioural science. Notable scientific awards include: IISE Annual Conference Best Student Paper Award (2024) Transportation Science Paper of the Year (2023) EJOR Best Paper Award (2023) Best European Journal of Operational Research Review Paper (2022) Best Paper Finalist at major logistics conferences Professor de Koster has supervised over 30 students and is actively involved in editorial service for top journals such as Transportation Science , Production and Operations Management , and International Journal of Production Research . He is chairman of Stichting Logistica and founder of the Material Handling Forum, contributing significantly to both academic and industry advancement in logistics. He leads research in advanced logistics labs focusing on robotic sorting, mobile fulfillment, and sustainable supply chains, often in collaboration with European institutions and industry partners.

• Operations Research
• Supply Chain Management
• Humanitarian Logistics
• +۳ مورد دیگر

Prof. Dick den Hertog is a Full Professor at Tilburg University's Department of Econometrics and Operations Research, part of the Tilburg School of Economics and Management (TiSEM). His research focuses on operations research methodologies with applications in humanitarian logistics, supply chain optimization, and robust decision-making under uncertainty. He collaborates with organizations like the UN World Food Programme to enhance operational efficiency in complex environments. Key research areas include robust optimization techniques, supply chain management in developing regions, and the integration of satellite data with machine learning for infrastructure analysis. His work addresses challenges such as food aid distribution, disaster response logistics, and predictive modeling for transportation systems in data-scarce areas. Notable projects include developing analytical tools for the WFP's supply chain planning and creating algorithms for weather-informed road speed prediction. He is affiliated with the Tilburg Sustainability Center and the Operations Research research group, contributing to both academic advancements and real-world impact through optimization solutions.

• Evolutionary Computation
• Genetic Algorithms
• Optimization
• +۵ مورد دیگر

Dirk Thierens is an Associate Professor in the Department of Computer Science at Utrecht University's Faculty of Science, specializing in Intelligent Systems within AI & Data Science. His academic career spans over 25 years, with continuous publications from 1996 through 2025, demonstrating sustained research activity and leadership in his field. He maintains an active research program with numerous collaborations, most notably with Peter A.N. Bosman, indicating a long-standing productive research partnership. Thierens' research focuses on evolutionary computation, particularly model-based evolutionary algorithms, genetic algorithms, and optimization techniques. His work has evolved from foundational genetic algorithm research in the late 1990s and early 2000s to more specialized model-based approaches in recent years, including significant contributions to Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEA). His expertise spans single-objective and multi-objective optimization, permutation problems, mixed-integer problems, and real-valued optimization. In recent years, his research has expanded into applications in machine learning, particularly semi-supervised learning and neural network optimization. His publication record shows a consistent output of high-quality research, with numerous papers in top conferences like GECCO and journals in evolutionary computation. His most recent work (2023-2025) demonstrates continued innovation in synthetic data generation, neural network combination techniques, and parameterless evolutionary algorithms. The breadth of his work spans theoretical algorithm development, benchmarking methodologies, and practical applications in healthcare and other domains. While no specific scientific awards are mentioned in the available information, his extensive publication record, tutorial contributions at major conferences, and sustained research productivity over multiple decades indicate recognition within the evolutionary computation community. His tutorial work at GECCO conferences suggests he is considered an authority on model-based evolutionary algorithms. Thierens maintains an active research laboratory focused on evolutionary algorithms and their applications, with recent work exploring the intersection of evolutionary computation and deep learning. His research continues to advance both theoretical understanding and practical applications of optimization techniques in complex problem domains.

Ward Romeijndersمشاهده پروفایل

استاد

• Operations Research
• Management Science
• Stochastic Programming
• +۷ مورد دیگر

Prof. Ward Romeijnders is a Full Professor in the Department of Operations Management & Operations Research at the University of Groningen. He holds leadership roles in academic organizations such as Secretary of the Stochastic Programming Society (COSP), Associate Editor of Mathematical Methods of Operations Research, and Board member of LNMB. His research focuses on stochastic programming, optimization under uncertainty, and applications in energy, logistics, healthcare, and finance. He has led projects like the NWO VIDI grant 'Discrete Decision Making under Uncertainty' (2023-2027) and previously the NWO VENI project on planning under uncertainty. Education: Doctorate in Operations Research (details unspecified). Research Interests: Stochastic optimization, risk-averse decision-making, integer programming, and applications in societal challenges. Recent publications span algorithmic advancements in Benders decomposition, robust optimization, and error-bound theories. He has received multiple awards, including the Gijs de Leve Prize (2015-2017) and Willem R. van Zwet Award (2016). As a teacher, he instructs advanced courses on stochastic programming and optimization under uncertainty. Grants: NWO VENI (2017-2020), NWO VIDI (2023-2027). Editorial Roles: Mathematical Methods of Operations Research, Euro WG on Stochastic Optimization.

• Artificial Intelligence
• Smart and Service Industry
• Mobility and Transport
• +۵ مورد دیگر

Marco Schutten is an Associate Professor at the Digital Society Institute of the University of Twente, affiliated with the Industrial Engineering & Business Information Systems department. His work bridges Artificial Intelligence , Transportation , and Operations Research , focusing on optimizing complex systems. Expert in Urban Logistics and Freight Transport Specializes in Mathematical Programming and Optimization Research interests include Vehicle Routing , Machine Scheduling , and Agent-Based Simulation . Key trends in his 15 most recent articles (2015–2025) involve: Dynamic scheduling under time constraints Urban logistics and smart city applications Heuristics for combinatorial optimization Integration of MILP and Simulation models No scientific awards, formal supervisory roles, or part-time appointments are explicitly mentioned.

Christopher Hojnyمشاهده پروفایل

استادیار

• Mixed-Integer Programming
• Symmetry Handling in Optimization
• Combinatorial Optimization
• +۳ مورد دیگر

Christopher Hojny is an Assistant Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology , specializing in combinatorial optimization. He contributes to the EAISI Foundational group and co-develops the academic solver SCIP . His research focuses on symmetry handling in mixed-integer programming , theoretical properties of integer programs, and algorithm development for combinatorial optimization. Recent work explores applications in graph neural network verification , clustering problems, and network coding through mixed-integer programming frameworks. Key publication trends show expertise in Symmetry detection and mitigation techniques Relaxation complexity theory Applications to machine learning robustness Decision diagram-based scheduling Scientific contributions include Proof systems for symmetry certification Topological bounds tightening in GNNs Stable set problem symmetry handling SCIP solver extensions He supervises PhD students Cédric Roy (NWO project on Local Symmetries) and Sten Wessel (co-supervised with Frits Spieksma), while Jasper van Doornmalen (2019-2023) investigated symmetry propagation algorithms.

Kees Jan Roodbergenمشاهده پروفایل

استاد

• E-commerce Logistics
• Warehouse Optimization
• Vehicle Routing
• +۳ مورد دیگر

Prof. dr. Kees Jan Roodbergen is a Professor of Quantitative Logistics at the University of Groningen and Director of the Center for Operational Excellence. He holds a MSc in Econometrics & Operations Research from the University of Groningen and a PhD from Erasmus University Rotterdam. His research focuses on E-commerce Logistics, Warehouse Optimization, Vehicle Routing, and Inventory Management, applying Operations Research techniques to practical challenges. He has led projects like PIONEER (2019), e-global (2017), and 'Towards Virtual Ports' (2015), securing external funding from TKI Dinalog, NWO, and Interreg IVB. He serves on the Scientific Advisory Council of the Global Cold Chain Foundation and has been involved in jury roles for e-commerce awards. His teaching spans courses on Value Chain Management, Facility Design, and Discrete Operations Research. Education: MSc from University of Groningen (Econometrics & OR), PhD from Erasmus University Rotterdam. Ancillary roles include editorial advisory boards for professional magazines and program boards for TRAIL Research School and Graduate Program Operations Management & Logistics. Research contributions emphasize practical applications, such as warehouse optimization methods integrated into management systems. Grants and Projects: Highlighted projects include PIONEER (TKI Dinalog), e-global (NWO), and collaborations with industry. His work addresses sustainable logistics, digital transformation, and operational efficiency. He has published in journals like Operations Research and Transportation Science.

M.B. Frankeمشاهده پروفایل

استاد

• Process Design and Optimization
• Mixed-Integer Optimization
• Process Optimization under Uncertainties
• +۷ مورد دیگر

Prof. M.B. Franke is a Professor of Process Design and Optimization at the Sustainable Process Technology (SPT) Group, part of the Faculty of Science and Technology (TNW) at the University of Twente. His academic career follows over 15 years of industry experience at Bayer and LANXESS in Germany and China, where he held roles such as process engineer, project manager, and global technology manager. He previously led Bayer's Process Modeling and Design group, contributing to digital transformation initiatives including Digital Twins for production plants. Education: Bachelor's/Master's in Chemical Engineering, Technical University (TU) Dortmund, Germany (1996–2001) PhD in Fluid Separations, TU Dortmund, Germany Research Interests: Advanced process design optimization, scheduling of chemical batch plants, mixed-integer optimization, and process modeling under uncertainties. His expertise spans distillation systems, discrete event simulation, and applications in biodiesel and agricultural biotechnology, including algae-based design. Awards: None explicitly mentioned in the provided text. Advising & Grants: No student advisees or grant details listed. His industry roles focused on process innovation and project management, with academic work centered on digital transformation in chemical engineering. Labs/Teams: Leading the Sustainable Process Technology (SPT) Group at the University of Twente, focusing on interdisciplinary approaches to process optimization and sustainable industrial practices.

Ilker Birbilمشاهده پروفایل

استاد

• Optimization Methods
• Data Science
• Decision Making
• +۳ مورد دیگر

Prof. Dr. Ilker Birbil is a Professor of AI & Optimization Techniques for Business & Society at the University of Amsterdam (UvA), affiliated with the Amsterdam Business School's Business Analytics section. He previously held professorships at Erasmus University and Sabancı University, focusing on optimization and data science. His research interests include interpretable machine learning, data privacy, and optimization methods in decision-making. Education: PhD in Operations Research from North Carolina State University Postdoc at Erasmus Research Institute of Management (ERIM), Netherlands Research Interests: Optimization in data science, interpretable AI, privacy-preserving algorithms, and decision-making systems. Recent work focuses on differentially private optimization and meta-learning techniques like LESS. Key Achievements: Recipient of multiple teaching awards at Sabancı University Affiliated researcher at OPTIMAL (Optimization for and with Machine Learning) Organized workshops on ML for optimization and mathematics of ML Grants & Teams: Leads the UvA group on Optimization and Machine Learning at LNMB. Collaborates on projects blending OR and ML, including privacy-aware algorithms and revenue management systems. Labs/Teams: OPTIMAL, UvA Optimization Group, and interdisciplinary teams in data analytics.

• Chemical Engineering
• Sustainable Chemistry
• Process Technology
• +۵ مورد دیگر

Aloysius G.J. van der Ham is a Lecturer in Sustainable Process Technology, focusing on chemical engineering and sustainable chemistry. His research bridges fundamental thermodynamics with industrial applications, particularly in renewable energy integration and resource-efficient processes. Research Focus: Dr. van der Ham investigates sustainable chemical transformations, including electrified hydrocarbon production, hydrogen storage via liquid organic carriers, ionic liquid-based metal extraction, and carbon capture optimization. His work emphasizes: Electrification of industrial processes (ethylene/propylene production) Advanced membrane reactor designs Economic feasibility of renewable technologies Thermodynamic modeling of separation systems Publication Trends: Recent articles (2023-2025) demonstrate a consistent focus on decarbonizing chemical manufacturing through electrification, membrane-assisted reactions, and circular resource recovery. Key themes include techno-economic analysis of sustainable processes and thermodynamic optimization for energy systems. Supervision: He has guided 5 research projects, though specific student names or grant details are unavailable in this profile.

Jasper Bakkerمشاهده پروفایل

پژوهشگر

• Network Design
• Electric Road Systems
• Fleet Renewal
• +۵ مورد دیگر

Jasper Bakker, MSc is an active researcher specializing in sustainable transportation infrastructure with a focus on electric road systems and fleet electrification strategies. His work bridges computer science applications with practical transportation solutions, contributing to UN Sustainable Development Goals related to clean energy and sustainable cities. His research interests center on network design for electric transportation infrastructure, with particular expertise in adoption potential analysis of electric road systems during early development stages. His work examines infrastructure investment strategies, fleet renewal processes, and the integration of electric trucks into existing transportation networks, employing computational methods to optimize system performance. His publication record shows a clear trajectory of increasingly sophisticated research on transportation electrification, with recent work focusing on strategic fleet replacement models using mixed-integer programming approaches. His 2025 paper in Applied Energy represents the most current work in this emerging field. Bakker has established productive collaborations with researchers including Lopez Alvarez, Veldman, and Buijs, suggesting affiliation with an institution focused on operations research and sustainable transportation solutions. His work demonstrates strong methodological rigor with applications to real-world decarbonization challenges in the freight transportation sector.

• Electric Power Distribution
• Load Profile Analysis
• Distribution Systems
• +۵ مورد دیگر

Mauricio Salazar Duque serves as a University Researcher in the Department of Electrical Energy Systems within the School of Electrical Engineering at Eindhoven University of Technology (TU/e). His work focuses on modernizing power distribution infrastructure through advanced computational techniques. His research expertise spans critical domains in electrical grid modernization: Electric Power Distribution & Load Profile Analysis Distribution System Optimization & Photovoltaics Integration Community Energy Storage & Reinforcement Learning Applications His publications consistently address UN Sustainable Development Goals related to affordable clean energy and climate action. Recent work demonstrates strong emphasis on data-driven modeling for grid resilience. Analysis of his 2023-2025 publications reveals a strategic shift toward generative AI for load profile synthesis and tensor-based mathematical formulations for multidimensional grid analysis. Key themes include handling PV integration challenges in low-voltage networks and developing visualization techniques for medium-voltage load anomalies. No scientific awards are documented in available records. He contributed to the 2018-2022 project 'Decentrale Optimalisatie en Sturing van Elektriciteitsdistributienetten' as a research member and supervised at least one Master's thesis on transfer learning for medium-voltage network measurements. His research collaborations extend through TU/e's press engagements including National Growth Fund initiatives and mobility systems workshops. Salazar Duque operates within TU/e's Electrical Energy Systems research group, collaborating with Prof. J.G. Slootweg and international partners on smart grid innovation projects.

Renata Sotirovمشاهده پروفایل

استاد

• Optimization
• Operations Research
• Mathematical Programming
• +۳ مورد دیگر

Renata Sotirov is a Full Professor at Tilburg University's Department of Econometrics & Operations Research within the Tilburg School of Economics and Management. She holds a PhD in Mathematics and has held academic positions including Associate Professor (2009–2014) and Assistant Professor (2007–2009). Her research focuses on optimization, operations research, and semidefinite programming with applications in combinatorial optimization and discrete mathematics. She has supervised PhD students such as Dr. Ning Ma and Dr. Uwe Truetsch and collaborates internationally, including with institutions like the University of Melbourne and McMaster University. Her work includes pioneering contributions to semidefinite relaxation techniques, integer SDPs, and algorithmic optimization. Recent projects involve the Chvátal-Gomory procedure for integer SDPs and complex cut polytopes. She has served as a research coordinator, director of graduate studies, and treasurer for her department. Her expertise spans teaching operations research methods, combinatorial optimization, and quantitative business analytics.

• Exploratory Data Analysis
• Dimension Reduction
• Cluster Analysis
• +۷ مورد دیگر

Michel van de Velden is an Associate Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. His work bridges statistical methodology and data science, with a focus on exploratory techniques and machine learning. His research interests lie primarily in exploratory data analysis , with a focus on dimension reduction , cluster analysis , and data visualization . He also actively contributes to supervised machine learning , particularly using tree-based methods such as random forests. His methodological work often addresses challenges in categorical data and mixed-type variables . The recent articles highlight a strong trend in developing exact and unbiased statistical methods for clustering and distance computation. His work spans correspondence analysis , hierarchical clustering algorithms , and software tools for mixed data, reflecting a blend of theoretical rigor and practical implementation. He has served on the editorial boards of Computational Statistics and Statistical Papers , contributing to academic peer review and publication quality. Michel van de Velden has supervised academic work and contributed to open datasets and software, indicating active mentorship and collaboration. His research is supported by ongoing publications and editorial activities, suggesting sustained engagement in the academic community. He is involved in developing software such as manystat and datasets related to categorical data visualization, indicating leadership in reproducible and open research practices.

• Mathematical Programming
• Correspondence Analysis
• Hierarchical Clustering
• +۷ مورد دیگر

Rick Willemsen is a Lecturer in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. His academic work lies at the intersection of mathematical programming, statistical data analysis, and computational optimization. Research Interests: His primary research focuses on correspondence analysis, hierarchical clustering, and dimension reduction techniques. He employs mathematical and mixed integer programming to develop exact and inverse solutions for statistical problems involving categorical data and contingency tables. His methodological contributions aim to improve the accuracy and interpretability of clustering and dimensionality reduction methods in econometric and data science applications. Publication Trends: Over recent years, Rick has published on exact algorithms for hierarchical clustering, uniqueness conditions in correspondence analysis, and novel optimization approaches for inverse problems. His research consistently integrates theoretical rigor with practical computational implementation, as evidenced by both journal articles and software outputs. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no public information available on students advised or grants received. However, his recent completion of a doctoral thesis suggests he is in the early stage of his independent academic career. Labs and Research Teams: Rick Willemsen collaborates closely with researchers such as Michel van de Velden and Willem van den Heuvel, likely within the Data Science or Quantitative Methods group at Erasmus School of Economics. His work appears to be part of a broader effort in developing optimization-based tools for statistical data analysis.