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
Uncertainty
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