The focus is on a two-stage city logistics system: in the first stage, multiple service providers cooperate using multimodal transport – such as cargo trams or larger trucks – to deliver goods to urban micro-hubs (so-called satellites). From there, small, environmentally friendly vehicles handle the last mile to customers. Disruptions occurring in the first stage can significantly impair onward transport and affect all involved providers.
The research team will first develop and mathematically model different recovery measures. These approaches will then be integrated into a tactical planning model. Using stochastic optimization techniques, the aim is to create robust, tactical plans. A particular emphasis is placed on developing specific methods that can efficiently generate feasible solutions in real time.
In addition, machine learning methods such as decision trees will be used to enable immediate operational responses to disruptions. The developed approaches will be validated with real data and simulation scenarios to analyze how factors such as the choice of transport modes, cooperation models, and vulnerability to disruptions affect resilience strategies.
ReSCueD will run for three years and makes an important contribution to creating resilient urban logistics.