We consider science to be a servant of society. In line with this perception, we strive to conduct research that relates to central societal challenges, e.g., mobility systems. In general, we are particularly active in the following fields.

Mobility & Transportation Systems

Mobility and transportation systems are one of the center pieces of our society, enabling flows of goods and people, and economic growth. However, with increasing urbanization and e-commerce, today’s transport infrastructures are reaching limits and cities all around the world struggle with congestion and exceeded emission thresholds. We focus on both people and goods transportation to provide decision support in shaping sustainable future transportation systems.

Currently, we focus especially on electric logistics fleets including network design, (fair) ride sharing algorithms, city center regulations, sector integration, and autonomous mobility on demand systems. Herein, we apply a variety of methods ranging from game theoretical approaches to matching algorithms and metaheuristics.

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Supply Chain & Service Operations Management

Nowadays, optimization is essential in any supply and value chain. Manufacturers use algorithms to improve their production plans. At airports, complex aircraft scheduling and baggage handling processes are supported by operations research tools. E-commerce retailers could not offer same-day and two-hour deliveries without a whole orchestra of efficient algorithms. We develop state of the art algorithms for a multitude of application cases from different industries, often in collaboration with companies.

Currently, we are especially active in picker routing and warehouse management for e-commerce environments, value-based supply chain and production planning, airport operations, as well as modular, AGV assisted assembly lines in the automotive industry.

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Data Science, Machine Learning, & Math Programming

Recently, data science evolved as a buzzword comprising a multidisciplinary field in between machine learning, statistics, and optimization. In this field, techniques are developed to gain insights by processing huge amounts of structured or unstructured data. Elements of such techniques can be found throughout professional applications and in our daily live, amongst others in automated product recommendations in e-commerce, maintenance scheduling in manufacturing, or fraud detection in banking and finance. We develop new state of the art approaches in this field, often at the interface between classical operations research and machine learning. We believe that instead of cannibalizing each other, there exists a vast symbiosis potential between both disciplines if understood and applied correctly.

Currently, we work especially in the fields of spatio-temporal forecasting, non-convex support vector machines, utilization of classifiers in large-scale optimization, manipulation detection in (social) networks, as well as approximation and online algorithms.

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