Troels Martin Range

Troels Martin Range is an Associate Professor at the Department of Clinical Research, University of Southern Denmark, affiliated with Odense University Hospital (OUH) and the Research Unit of Emergency Medicine in Odense. His work bridges operational research and clinical medicine, focusing on optimizing healthcare delivery systems.

His research interests lie primarily in operational research, stochastic optimization, and healthcare analytics. He investigates emergency department patient flow, surgery scheduling, and resource allocation using advanced mathematical models such as column generation, simulation modeling, and machine learning techniques. His work often integrates real-world data including meteorological and calendar variables to improve forecasting accuracy.

The analysis of his recent publications reveals a consistent trend toward applying rigorous mathematical and computational methods to solve complex problems in healthcare operations. His work spans both theoretical contributions in Mathematical Programming and practical implementations in hospital settings, particularly in emergency and surgical care. He frequently employs stochastic modeling, decision forests, and neural networks to address uncertainty in medical systems.

• Editor, International Journal of Advanced Operations Management
• Member, Dansk Selskab for Operationsanalyse (DORS)
• Guest Lecturer on hospital staff rostering and resource planning

He has taught courses in operations analysis and has supervised academic work, though specific advisees are not listed. His research has been supported through academic collaborations and implementation projects in hospital environments. He is actively involved in knowledge transfer between academia and healthcare institutions.

Dr. Range is part of a research network focused on emergency medicine and operational efficiency, contributing to both national and international collaborations. His lab or team context centers on the Research Unit of Emergency Medicine at OUH, where data-driven approaches are developed to enhance clinical decision-making and system performance.