(summary)
CONTEXT
This control tower will provide end-to-end tracking of all the logistics flows in the network. In this case, the NEXUS team intends to use Big Data Analytics, Business Intelligence and Operations Research based models over a multi operator network that will enhance logistics predictability to the next level.
Components to be developed: - Merchandise tracking portal, providing the interface for users to perform not only traditional tracking and tracing but also to provide access to forecast elements and performance indicators. The portal should also include evaluation/satisfaction elements that will feed back the algorithms. - Predicative models of transhipment points based on machine learning techniques, including the aspects described above. - Predictive model of transport times, using optimization algorithms and machine learning. - Shipment Digital twin API - Decision Support Systems for managing the operations of multimodal transport networks through a multi-criteria approach - Dashboards and simulation models for synchro-modal transport networks.
This task has the following subtasks:
- #2.1.1: Time series streaming modelling of logistic networks
- #2.1.2: High-performance logistics networks
- #2.1.3: A visual tool for synchronised the logistic networks
- #2.1.4: Empowering ML models with Explainable and Interpretable AI
- #2.1.5: Risk Awareness Model Decision Support Tool
- #2.1.6: Identification of impact factors on metrics of logistic network processes
- #2.1.7: Application development of the multimodal control tower
- #2.1.8: Simulation model for the multimodal control tower