Research projects

In our various research projects, we develop new mathematical models and computational methods for efficient resource allocation and coordination among multiple parties in dynamic logistics networks, transportation, and mobility systems. Our projects address the methodological challenges of large-scale problems, multiple decision-makers, and uncertainty.

Current research directions include:


Large-scale problems

Aggregation in hierarchical scheduling

Conflict-free routing in flexible manufacturing systems

Energy-efficient scheduling

Freight forwarding and optimal routing in the logistics industry

Locating charging stations with mobility and grid constraints

Vehicle routing with flexible and resource-constrained delivery locations


Multiple decision makers

A game-theoretical perspective on (autonomous) mobility-on-demand systems

Complexity and algorithms for transportation markets with budget constraints

Coordination mechanisms for mid-term resource allocation in production networks

Equilibrium analysis of transportation tenders

Queuing models of freight matching platforms

Stochastic dynamic integer programming games in logistics



A prescriptive data-driven optimization framework for future mobility systems

Machine learning and operations research for hierarchical planning

Online appointment scheduling with robust service levels

Optimization for dynamic service routing with driver-customer consistency

Policy learning for stochastic inventory and transportation problems

Process mining for transportation reoptimization