Carl Henrik Andersson is a Professor at the Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU). His research centers on optimization methodologies for complex industrial and logistical challenges. Research Focus Andersson's work spans: Operations Research : Developing decomposition algorithms and heuristic approaches Scheduling Systems : Integrated project-personnel scheduling Sustainable Transport : Micromobility planning via simulation tools His 2024 publications demonstrate consistent focus on enhancing optimization techniques for resource allocation. Achievements Notable recognition: Glover-Klingman Prize (2014) for influential work in network optimization Projects He led key initiatives: Design and Experimental Analysis of Novel Integrated Optimization Methods DOMinant II: Discrete Optimization Methods in Maritime/Road Transport Both projects advanced practical optimization methodologies.
Mohamed Ben Ahmed serves as an Associate Professor in Quantitative Logistics at Molde University College's Logistics Department, specializing in optimization and decision support systems for complex logistics networks. His work integrates mathematical modeling with real-world transportation challenges across maritime, airline, and urban mobility sectors. Educational Background: PhD in Quantitative Logistics, Molde University College (2023) - Thesis: 'Analysis of Integrated Planning Models in Logistics' MSc in Industrial Engineering, National School of Engineering, Tunis (2013) BSc in Physics and Chemistry, Preparatory Institute of Engineering Studies, Monastir (2010) His research focuses on developing robust solutions for logistics under uncertainty, with particular expertise in hybrid algorithms combining exact and heuristic methods. Key areas include sustainable micromobility systems, green maritime transportation, automated container terminal operations, and integrated airline scheduling. Current work emphasizes automation in logistics processes and decision-making frameworks resilient to disruptions. Analysis of his 15 most recent publications (2017-2025) reveals a strong trend toward practical applications of optimization in emerging logistics domains, particularly micromobility simulation and green technology adoption. His work consistently bridges theoretical algorithm development with industry-relevant case studies, showing increasing emphasis on sustainability metrics and real-time decision support systems. No scientific awards are currently listed in available sources. His research is conducted within the Planning, Optimization and Decision Support group, focusing on mathematical modeling of logistics networks. While specific grant information isn't provided, his publications indicate collaboration with major industry partners in maritime and aviation sectors. Current projects include developing open-source simulation tools for micromobility planning and optimization frameworks for automated container terminals.
Ketil Danielsen serves as an Associate Professor within the Logistics Department at Molde University College, Norway. His extensive teaching portfolio includes: Computer Communications Operating Systems IT Operations Databases Algorithms and Data Structures Programming Web Development Statistics Simulation His academic role bridges computer science with logistical applications through computational methodologies. Dr. Danielsen's research centers on computer science and operations research with emphasis on simulation, programming, and optimization. He investigates network resource allocation, quality of service for multimedia services, and combinatorial optimization for routing problems. His work demonstrates strong interdisciplinary connections between theoretical computer science and practical logistical solutions, particularly in transportation and telecommunications infrastructure. Analysis of his publication record reveals consistent focus on optimization algorithms and networking protocols. Recent work explores tabu search methodologies for integer programming and vehicle routing applications, while foundational contributions address MPLS networking protocols, service disruption recovery, and auction-based resource allocation. This trajectory shows sustained innovation in solving complex logistical challenges through computational approaches. Scientific Awards: No awards are documented in available sources. Advising and Grants: Information regarding student supervision and research funding is not provided in current documentation.
Paulo Cesar Ribas is a Professor in Energy Logistics at Molde University College (HiMolde), Norway. His expertise lies in optimizing supply chains for energy sectors, especially offshore wind and oil & gas industries. He holds a Ph.D. in Electrical Engineering and Industrial Informatics from UTFPR, Brazil, with prior roles including 16 years at PETROBRAS as an Operational Research Analyst, focusing on supply chain planning and optimization. Education: Ph.D. (2012): Electrical Engineering and Industrial Informatics, UTFPR, Brazil Master's (2003): Production and Systems Engineering, PUC/PR, Brazil Bachelor's (1996): Industrial Electrical Engineering, UTFPR, Brazil Specialization (1999): Business Management (ERP Systems), PUC/PR, Brazil Research Interests: Ribas focuses on energy logistics optimization, including offshore wind energy systems, oil & gas supply chains, and low-carbon emission strategies. His work combines discrete event simulation, mathematical modeling, and advanced algorithms to solve complex operational challenges. Recent studies address offshore platform vessel management, pipeline decommissioning, and wind farm maintenance logistics. Key Contributions: His publications emphasize optimization models for supply chain planning, vessel scheduling, and multi-objective algorithms. Notable projects include fleet management strategies for Brazilian offshore operations and helicopter rescheduling in oil industries. He leads the Energy Logistics Research Group (EneLog), collaborating on cutting-edge solutions for sustainable energy logistics. Professional Experience: 2006–2022: PETROBRAS R&D Center – Logistics Research Analyst 2019–2022: Guest Professor at HiMolde 2013–2018: Associate Professor at Veiga de Almeida University, Brazil Labs/Teams: Active contributor to the Energy Logistics Research Group (EneLog), focusing on innovative logistics solutions for renewable and traditional energy sectors.
Sebastian Urrutia is a Professor in the Faculty of Logistics at Molde University College (HiMolde), specializing in Operations Research and Optimization. He holds a PhD in Computer Science from Pontifical Catholic University of Rio de Janeiro (2005) and a BSc in Computer Science from the University of Buenos Aires (2001). His research focuses on Integer Programming, Approximation Algorithms, Graph Theory, and applications in Healthcare Logistics, Transportation, and Sports Scheduling. Key research interests include optimization methods for healthcare systems, maritime inventory routing, and algorithmic solutions for complex scheduling problems. His work bridges theoretical advancements in combinatorial optimization with practical applications in logistics and transportation. Recent publications emphasize Benders decomposition for healthcare logistics, polynomial-time graph algorithms, and maritime routing optimization. He has contributed to over 40 peer-reviewed articles in journals like European Journal of Operational Research and Networks . Urrutia’s academic contributions include developing exact and heuristic methods for NP-hard problems, with notable work on the Double Traveling Salesman Problem with Multiple Stacks and token swapping algorithms on cographs.
Arild Hoff is a Professor of Quantitative Logistics at Molde University College, Faculty of Logistics. He teaches courses including LOG500 Management Models and Operations Research (Bachelor - Fall semester) and LOG530 Distribution Planning (Bachelor - Spring semester). Previously, he has taught several subjects within informatics, inventory and production management and exact optimization methods, both at bachelor and master level. His educational background includes: PhD in Logistics – Molde University College (2006) Cand. Scient. – University of Bergen (1998) Cand. Mag. – Molde University College (1994) Marketing – Trondheim Business School (1991) Business Economist – Trondheim Business School (1987) Professor Hoff's research focuses on operations research, planning, optimization and decision support, with particular emphasis on solution methods for combinatorial optimization problems, such as vehicle routing and hub location. His PhD thesis addressed "Heuristics for Rich Vehicle Routing Problems," exploring search methods for solving advanced vehicle routing problems with special constraints. His recent publications demonstrate a strong focus on maritime logistics, inventory routing problems, and optimization methods under uncertainty. His work spans applications in aquaculture, disaster relief, waste collection, and transportation planning, with a consistent emphasis on developing and applying advanced optimization techniques to complex real-world problems. Professor Hoff is actively involved in research groups including Energy Logistics (EneLog) and Planning, Optimization and Decision Support, with additional interests in aquaculture and other marine industries.
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.
