Research Group Quantitative Methods for Logistics

Digitization of processes in business contexts happens increasingly by relying on modern, intelligent information systems to support operational decisions. These decision support systems must calculate optimal decisions in a fast and largely automated way, before recommending them to the decision maker. The development of such systems represents a significant challenge - in particular with respect to the dynamic, volatile and uncertain nature of today’s operational processes.
 
The activities of Prof. Dr. Stephan Meisel’s research group Quantitative Methods in Logistics aim at solutions to this challenge. The group’s primary research focus is on using, developing and integrating quantitative methods in order to enhance the performance of decision support systems.
 
Besides research on systems for conventional logistics processes, the group has a particular focus on decision support for processes in the energy industry. Currently the main research areas of the group are:
  • Energy Storage Management: The utilization of renewable energy has been increasing rapidly all around the world. However, renewable energy sources like wind and solar can typically not be fully controlled. As a consequence, large-scale energy storage systems like grid-level batteries have been receiving increased attention. Intelligent decision support systems can help to fully take advantage of the flexibility provided by energy storage. The systems aim at continuously recommending (dis-)charging decisions while anticipating the volatilities of energy prices and energy generation, as well as future (dis-)charging decisions.
  • Efficient E-Mobility: Electric vehicles are supposed to replace vehicles with combustion engines almost completely on the long run. However, the question of whether or not this transition is going to work out also depends on the economic efficiency of e-vehicles. Decision support systems can help to fully leverage the economic potential of e-vehicles in all of their contexts of use. The systems aim at continuously recommending operational decisions to vehicle users and aggregators while anticipating the state of smart grids, energy price volatility as well as future decisions.
  • Dynamic Vehicle Routing: E-Commerce, globalized supply chains, and increased demand for on-site service, lead to the fact that cost-efficient operation of delivery and service vehicles has become an essential factor of success for many companies. Due to a continuous stream of newly arriving customer orders, and of traffic updates, companies typically have to readjust their vehicles’ routes several times during a day. Decision support systems can help to make optimal decisions about adjustments, while anticipating both newly arriving information and future decisions.