Modeling and Calibration of Last-Mile Logistics to Study Smart-City Dynamic Space Management Scenarios.

This contribution presents a base case modeling and calibration for simulation studies of last-mile parcel delivery in urban areas to be compared with future sustainable and livable alternative scenarios. We formulate a two-stage mixed-integer programming optimization framework. In the first stage, stopping points are identified using a facility location problem. In the second stage, these stopping points are served through multiple vehicle tours by solving a capacitated vehicle routing problem. The parameterization and calibration are based on real data for a part of the Hanover Linden-Nord district, which includes the street network, the parcel demand of a large logistics service provider, and stopping distances derived from trajectories. The model is the basis for follow-up work in the area of 5G-enabled dynamic space management.