Dag Haugland

Dag Haugland is a Professor in the Department of Informatics at the University of Bergen, Norway, where he conducts research and teaches in optimization, particularly combinatorial and global optimization with applications in network flows, energy systems, and logistics. He is affiliated with the Optimization research group and maintains an active research profile with recent publications in top-tier journals.

Research Interests:
His work focuses on Combinatorial Optimization, Global Optimization, and Network Flow Models, applying mathematical programming techniques to real-world problems in offshore wind energy, gas pipeline transportation, wireless networks, and vehicle routing. His research emphasizes integer programming, polyhedral analysis, and algorithmic design.

Recent Research Trends:
His latest publications (2023–2024) address tighter bounds in broadcast time problems and hydropower scheduling, reflecting a continued focus on theoretical and applied optimization in communication and energy networks. Earlier works (2016–2020) explore pooling problems, offshore wind farm cable layouts, and portfolio optimization, demonstrating interdisciplinary reach.

Scientific Contributions:

• Extensive publication record in optimization and operations research.
• Supervision of multiple PhD and master’s students.
• Active involvement in conference proceedings and technical reports.

Advising and Grants:
Dag Haugland has supervised numerous students, including Marika Ivanova and Arne Klein, on topics such as offshore wind farm optimization and multicast tree problems. His work has been supported by Norwegian research funding, including the Research Council of Norway (project reference 249994). He contributes to academic service through conference organization (e.g., Norsk Informatikkonferanse) and collaborative research.

Labs and Teams:
He is a key member of the Optimization research group at the Department of Informatics, University of Bergen, which focuses on algorithmic and mathematical approaches to complex decision problems in engineering and industry.