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• Combinatorial Optimization
• Quadratic Programming
• Binary Optimization
• +5 more

Cédric Elloumi is a Professor at the CEDRIC Laboratory within Conservatoire National des Arts et Métiers (CNAM), specializing in combinatorial optimization and mathematical programming. With a continuous publication record since 1992, he has established himself as a leading researcher in quadratic programming, binary optimization, and facility location problems. His research interests focus on developing exact and approximate methods for discrete optimization problems, particularly through convex reformulation techniques. Elloumi has made significant contributions to the p-center and p-median problems, quadratic assignment problems, and more recently, quantum-inspired optimization methods. His work bridges theoretical advancements with practical applications in network design, energy systems, and telecommunications. Analysis of his recent publications (2022-2025) reveals a continued focus on facility location problems, with increasing attention to robust optimization under uncertainty and emerging applications in quantum computing. His research demonstrates consistent methodological innovation, particularly in reformulation techniques that transform difficult non-convex problems into tractable forms. Throughout his career, Elloumi has maintained extensive collaborations with researchers including Billionnet, Lambert, Alès, and Plateau, resulting in numerous publications in top-tier optimization journals such as Journal of Global Optimization, Computers and Operations Research, and Mathematical Programming.

• Global Optimization
• Competitive Facility Location
• Discrete Optimization
• +4 more

Assoc. Prof. Dr. Algirdas Lančinskas is a Senior Researcher and Associate Professor at Vilnius University's Faculty of Mathematics and Informatics, working within the Global Optimization Group at the Institute of Mathematics and Informatics. His academic career spans over a decade at Vilnius University, where he has progressed from Junior Researcher to his current position as Associate Professor and Chief Researcher on multiple projects. He maintains an active international research profile with collaborations across Europe. Doctor of Physical Sciences (Informatics, 09P), Vilnius University Institute of Mathematics and Informatics, 2013 Dissertation: Parallelization of random search global optimization algorithms Dissertation supervisor: Prof. Dr. (HP) Julius Žilinskas Dr. Lančinskas specializes in global optimization, particularly competitive facility location problems, discrete optimization, and parallel computing. His research bridges theoretical optimization methods with practical applications in business, spatial economics, and public health. He has made significant contributions to nature-inspired optimization heuristics, developing novel algorithms for solving complex location problems with applications ranging from business expansion strategies to pandemic testing protocols. His work often involves multi-objective optimization approaches and the development of efficient parallel algorithms to handle computationally intensive problems. Analysis of his recent publications reveals a strong focus on competitive facility location modeling, with particular expertise in discrete optimization problems where multiple competitors vie for market share. His research increasingly incorporates robustness considerations under uncertainty in customer behavior, reflecting the growing complexity of real-world location decisions. The integration of ranking-based approaches with traditional optimization techniques represents a distinctive methodological contribution across his work. Dr. Lančinskas actively supervises doctoral students and has served on multiple dissertation defense councils. His research is supported by significant funding from the Research Council of Lithuania and international collaborations through COST actions. He has led multiple research projects focused on optimization algorithm development and their applications. As a member of the Global Optimization Group, he contributes to Vilnius University's strong tradition in optimization research, collaborating closely with Prof. Julius Žilinskas and international partners across Spain, the UK, and other European institutions. His work exemplifies the institute's commitment to both theoretical advances and practical applications of optimization methods.

Mattias DahlView profile

Professor

• Systems Engineering
• Applied Mathematics
• Signal Processing
• +7 more

Mattias Dahl is a Professor at the Faculty of Engineering, Blekinge Institute of Technology, affiliated with the Department of Mathematics and Natural Sciences since 1993. His research spans systems engineering, applied mathematics, and their applications in simulation, optimization, and modeling of technical systems, particularly in intelligent transport systems (ITS) through collaborations with Swedish Transport Agency and Administration. He has developed measurement systems using drones and satellites, focusing on area-wide change analyses and commercialization of research outputs. Education: B.Eng. in Electrical Engineering, Chalmers University M.Eng. in Computer Engineering, Luleå University of Technology Licentiate in Telecommunication Theory, Lund University of Technology PhD in Applied Signal Processing, Blekinge Institute of Technology (2000) His research emphasizes optimization of technical systems, self-learning methods, and artificial intelligence, with industry collaborations resulting in patents in mobile communication and computer vision. Recent work includes AI-driven weed seed reduction, railway capacity optimization (KAJT), and charging station allocation for EVs. He has contributed to projects like ADAS and Combating Reindeer Poaching with Drones, while also reviewing grants for international journals. Key scientific awards include the Teknikbrostiftelsen scholarship and Vinnova verification funds. His 15 most recent publications focus on radar interference mitigation, traffic data analysis, drone calibration, and charging infrastructure optimization.

• Machine Learning
• Evolutionary Algorithms
• Swarm Intelligence
• +5 more

Mehdi Neshat is a Visiting Scholar at the Data Science Institute within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He holds a PhD in Engineering from the University of Adelaide (2016-2020) and has extensive experience as a research data scientist specializing in computational optimization methods applied to complex engineering and healthcare problems. His educational background includes: PhD in Engineering, University of Adelaide (2016-2020) Neshat's research focuses on developing and applying advanced computational methods to solve real-world challenges. He specializes in evolutionary algorithms, swarm intelligence, and machine learning techniques for optimizing renewable energy systems, particularly wave and wind energy converters. His work extends to healthcare applications including genomic analysis and medical diagnostics, as well as structural engineering optimization and smart building energy management. His interdisciplinary approach bridges theoretical algorithm development with practical implementation across multiple domains, demonstrating exceptional versatility in computational problem-solving. Analysis of his recent publications reveals a strong trend toward sophisticated ensemble methods and hybrid optimization approaches that combine multiple algorithms to overcome limitations of single-method approaches. His research shows increasing sophistication in handling multi-objective optimization problems, particularly in renewable energy systems where trade-offs between power output, system stability, and cost must be balanced. The geographical focus of his energy research centers on Australian coastal regions, with practical applications for wave and wind farm deployment. Neshat has received significant recognition for his research contributions: Back-to-back Best Paper Prizes at the GECCO conference (2019 and 2020), a CORE A-ranked international optimization and machine learning conference His collaborative research spans multiple institutions and disciplines. Previously, he served as a Postdoctoral Research Associate with the Genomic analysis team at the Australian Centre for Precision Health, Cancer Research Institute, University of South Australia, and as a Senior Research Fellow at the Center for Artificial Intelligence Research and Optimization, Torrens University Australia. His work demonstrates consistent engagement with multidisciplinary teams across engineering, computer science, and healthcare domains, with a strong emphasis on practical implementation of theoretical methods. At UTS, Neshat contributes to the Data Science Institute's research agenda, focusing on applying advanced computational methods to complex real-world problems where traditional analytical approaches fall short. His current work continues to expand the boundaries of optimization techniques for renewable energy systems while exploring new applications in healthcare analytics and structural engineering.

Karim TamssaouetView profile

Associate Professor

• Scheduling
• Inventory-Transportation Integration
• Operations Research
• +3 more

Karim Tamssaouet is an Associate Professor at BI Norwegian Business School's Department of Accounting, Auditing and Business Economics. His academic trajectory includes positions as Assistant Professor at BI (2020-2024), Postdoctoral Researcher at École des Mines de Saint-Étienne (2019-2020), and R&D Engineer at STMicroelectronics (2016-2019). He holds a PhD from École des Mines de Saint-Étienne, with additional graduate degrees from Université Paris Dauphine and Ecole Nationale Polytechnique. His research specializes in optimization methodologies for industrial challenges, with core interests in: Scheduling algorithms for job-shop and flexible manufacturing systems Integrated inventory-transportation modeling Metaheuristic design (scatter search, neighborhood structures) Dynamic lot-sizing with logistics constraints Bayesian network learning for decision optimization Publication analysis reveals consistent focus on operational efficiency in manufacturing and supply chains, with recent expansions into machine learning applications for optimization. Methodological contributions include novel neighborhood frameworks for scheduling problems and multi-objective approaches for semiconductor production. He currently teaches Business Optimization and Supply Chain Analytics, bridging theoretical operations research with business applications. No research lab affiliations, scientific awards, or supervised students are documented.