The logistics sector, as well as the passenger transport, is currently subject to transformation processes. The shift is caused on the one hand, by an increasing digitalization and the development of electro mobility that create new options for the transport chain. On the other hand, the demand for delivery is increasing and challenges the logistics sector time and time again. This includes for example higher requirements in terms of delivery times, punctuality and flexibility of deliveries. Simultaneously, climate-friendly logistics gains increasing importance.
To face these challenges, new concepts are investigated in various projects together with partners from the logistics sector, the industry, from sciences and management. The projects range from bundling deliveries, using transport drones and cargo bikes to the conception of new transport chains such as the development of city hubs with the last mile delivery.
The focus of these projects is mostly on the optimization of the actual transport chain. The requirements on the vehicle and thus the vehicle configuration are, for the most parts, secondary and very specific for each application case.
The project Smart.Vehicle.Configuration deals with the analysis of vehicle parameters in the field of logistics. The aim is to develop a flexible simulation tool that can map different delivery systems and evaluate respective key performance indicators.
The key performance indicators in the analysis are:
- The transport volume in relation to the fleet size
- The battery size in relation to charging speed
- Tour attributives such as distance, time and punctuality
- The effect of the traffic system as a whole (for example the impact on traffic performance)
The analysis of key performance indicators is conducted in an agent-based simulation that enables an easy adaptation for different delivery processes and locations. The simulation is performed within a MATSim model. To do so, suitable user cases in the delivery sector are identified such as the supply of end costumers with food. Then, the supply chain is implemented methodologically. By varying input parameters (e.g. fleet size, service area etc.) different scenarios are calculated. The evaluation of the different scenarios then provides the optimized vehicle parameters.