Digital Logistics Laboratory@TUM (DiLoLab)

Digitalization and technology innovations drive future logistics. In the new DiLoLab@TUM, we realize applied projects with the industry embedded in an international research network in the following project areas:

• AI-Log: Artificial Intelligence, Operations Research, and Machine Learning for Computational Logistics
  The use of Artificial Intelligence algorithms is seen as a major acceleration in logistics optimization, it complements classical Operations Research approaches. We combine fundamental research on machine learning algorithms with logistics domain knowledge, apply competitive reinforcement learning for real-time optimization, and promote data-driven approaches for integrated forecasting and decision making.

• PlatOn: Platforms and Collaborative Planning in Online Logistics
  Supported by the share-economy concept, new disruptive business models in freight transportation that need decision support for matchmaking emerge. However, collaborations often require the sharing of large amounts of data and centralized planning, which raises concerns and hinders adoption. We develop decentralized planning approaches with limited data-sharing requirements by using encryption and edge computing to take advantage of the benefits of collaborative logistics.

• QuantumLog: Quantum Engineering for Logistics Applications and Optimization
  Advances of quantum computing offer new opportunities to solve complex logistics optimization problems, but they require a different approach to the modeling and guiding of the solution process in logistics application.

The laboratory offers cooperation opportunities for those in the industry who are interested in joint development and research opportunities for postdoctoral and PhD researchers and student involvement in project work and master theses. If you are interested, contact us for further information under dilolab.wi@tum.de.