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TU Berlin

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Finalized research projects

MODULUSHCA

With the Modulushca project, the Chair of Logistics at TU Berlin together with partners from Europe, North America and the „Physical Internet Initiative“, is taking part in the development of a strong european logistics network, modeled after the internet. more to: MODULUSHCA

Research Study: Logistics Success Strategies

As part of the Federal Government’s Showcase Regions for Electric Mobility, the Federal Ministry of Economics and Technology is funding the Smart e-User project – one of 30 core projects taking place in Berlin Brandenburg. more to: Research Study: Logistics Success Strategies

RouteCharge

The aim of this project is to open up medium distances (300 km) for the transport of goods with electrically driven commercial vehicles and to integrate long-distance supply chains into distribution logistics with electric commercial vehicles. This is intended to broaden the possible range of application of the eNFZ from the fleet operator's point of view, thus achieving further progress in the economic efficiency of the eNFZ. Field tests to date have shown that the vehicles can only be operated within a tight logistical corset - typically as an inner-city distribution vehicle with low tour variance. The aim is therefore to develop and implement a concept that guarantees the fleet operator a freedom of disposition comparable to that of a diesel vehicle. This should improve the economic acceptance of the eNFZ, so that the fleet share of electric vehicles can grow more strongly than before. more to: RouteCharge

Smart e-User concept for electric mobile City Logistics

As part of the Federal Government’s Showcase Regions for Electric Mobility, the Federal Ministry of Economics and Technology is funding the Smart e-User project – one of 30 core projects taking place in Berlin Brandenburg. more to: Smart e-User concept for electric mobile City Logistics

SMECS

In the SMCES project an intelligent assistance system for the maritime transport chain was developed by applying machine learning methods. The implemented IT system predicts the arrival time (ETA) of intermodal container transports from the consignor to the seaport with a high degree of accuracy and thus proactively detects conflicts in the adherence to the planned process sequence. By providing disruption-related measures, the actors in the logistics chain are enabled to carry out the transports reliably and efficiently. more to: SMECS

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