The ANT/OR – Antwerp Operations Research Group of the University of Antwerp is dedicated to the study and development of planning algorithms across all decision-making levels: strategic, tactical, operational, and real-time. Our research aims to support complex planning environments with algorithmic solutions that are both practically relevant and methodologically rigorous. We focus on a broad range of application domains, each of which poses unique challenges and opportunities for innovation in operations research.
A significant part of our work lies in (urban) logistics and supply chain management. We address problems of urban supply chain and logistics planning while considering mobility, sustainability, and the efficient distribution of goods, with particular attention to parcel and grocery delivery in densely populated urban areas. Our research explores how companies can collaborate horizontally to share resources, reduce emissions, and improve service levels. We investigate the impact of real-time data and dynamic conditions on planning decisions, and develop algorithms that adapt to changing demands and traffic patterns. Through these efforts, we contribute to the design of smarter, more resilient, and environmentally conscious logistics systems.
Another key application area is space logistics and the planning and scheduling of satellite operations. As satellite constellations grow and missions become more complex, there is a growing need for efficient coordination of communication windows, ground station usage, and orbital maneuvers. We develop planning algorithms that address these challenges by optimizing the use of limited resources in highly constrained and dynamic environments. Our work in this domain pushes the boundaries of classical operations research and opens up new perspectives on how optimization techniques can be applied beyond terrestrial systems.
We also focus on non-industrial planning problems, where societal impact is often high and traditional cost-based optimization is insufficient. Examples include the optimal dispatching of ambulances, scheduling of nurses, routing of police patrol cars, and repositioning of bikes in shared mobility systems. These problems are characterized by their operational complexity and the need to balance efficiency with fairness, responsiveness, and accessibility. Our research in this area contributes to the development of decision support tools that improve the quality of public services while taking into account practical constraints and policy goals.
In more traditional operations management settings, we work on problems related to production planning, plant design, and warehousing. These include the allocation of tasks and resources in manufacturing systems, the layout and configuration of production facilities, and the organization of storage and picking processes. Even in these well-established fields, we develop novel algorithmic approaches that handle uncertainty, account for multiple objectives, and enable flexible, adaptive decision-making. Our goal is to bring innovation to core industrial processes through advanced optimization techniques.
Methodologically, our expertise lies in optimization, mathematical modelling, and metaheuristics. We not only apply metaheuristics to solve planning problems, but also study how they work, how they can be evaluated scientifically, and how their performance can be improved. Our work contributes to a deeper understanding of the foundations of metaheuristics and their responsible use in research and practice.
In addition, we study and develop methods in multi-criteria decision analysis (MCDA), enabling informed decision-making in environments with multiple stakeholders and conflicting objectives. These tools are particularly valuable in complex, real-world contexts where trade-offs must be made between economic, social, and environmental concerns.