Abstract: Producing and tuning an integrated vessel control model to meet decarbonization goals includes loading an base performance model for a vessel and corresponding observed carbon emissions from the vessel resulting from a set of control inputs for the vessel. A decarbonization model also is selected. A new integrated performance model is computed within a digital twin of the vessel modeling new performance metrics resulting from the deployment of accessories in the decarbonization model, and the new metrics are compared to minimum performance standards for the vessel. When a performance metric fails to meet the minimum standard, a corresponding accessory is identified as impacting the failing performance metric and an adjustment is incrementally applied to the accessory until the failing performance metric is determined to meet the minimum standard. Then, the new integrated performance model is simulated to predict resulting carbon emissions which is stored in the new integrated performance model.

Abstract: Physical Internet (PI) dynamic principal interface node (PIN) port selection includes selecting a primary maritime port as a PIN in a routing of freight aboard a sea going vessel from an origin node to a destination node in a PI model and receiving a disruption event in the PI model indicating an inability of the vessel to berth at the primary maritime port. A cluster of alternative PINs is determined in connection with the destination node of the PI model and a routing score computed for each alternative PIN based upon a cost of routing the freight through each alternative PIN. Finally, a new routing is established in the PI model utilizing an optimal alternative PIN in lieu of the selected PIN based upon a corresponding routing score, and a message is transmitted to the vessel to divert to a secondary maritime port associated with the optimal alternative PIN.

Abstract: The optimized dynamic scheduling of barges in inland waterways includes geolocating a set of barges positioned on an inland waterway and receiving current data for the inland waterway at each geolocation of each barge in the set. An estimated time of arrival (ETA) for each barge is retrieved from a table at a location for each barge along the inland waterway at which point the barge unloads onboard freight rendering the barge available to receive transport of new freight. Each ETA is modified for each barge according to the received current data at multiple different positions along a route in the inland waterway. Finally, an availability table for the barges in the set is constructed based upon each modified ETA so that barge availability queries received from over a computer communications network can be responded to with availability information stored in the availability table.

Abstract: Recursive multi-criteria Physical Internet (PI) freight orchestration includes the loading a hierarchy of connected nodes and specifying a starting node and ending node within the hierarchy. Different combinations of the connected nodes are determined which connect the starting node and the ending node, each defining a corresponding routing of the freight in a set of possible routings. Thereafter, for each corresponding routing of the freight, a route value is recursively computed from the starting node through the ending node and across different intermediate nodes of the routing for the freight connecting the starting node and the ending node. Each recursively computed route value is processed in order to select, amongst the possible routings, a selected routing for transporting the freight to the geographic destination and a record can be written with the selected routing for transporting the freight to the geographic destination.

Abstract: Critical infrastructure (CI) protection blueprint generation in an interdependent CI architecture includes constructing a digital twin of a heterogeneous collection of CI elements associated with respectively different services provided to a common community. Thereafter, hypothetical sensor data is specified in the digital twin for a target CI element in the hierarchy. In response, sensor data is read for other CI elements dependent upon the target CI element so as to identify impacted CI elements. For each impacted CI element, additional sensor data is read for further CI elements in the hierarchy dependent upon the impacted CI elements and the process repeats until no additional impacted CI elements are identified. A listing of all impacted CI elements is written to a blueprint for the hierarchy in association with the hypothetical sensor data in order to define a cascading effect of the hypothetical sensor data upon the hierarchy within the digital twin.

Abstract: Critical infrastructure (CI) blueprint selection for optimized response to state changing conditions includes defining a hierarchy of interdependent CI elements of a community in a digital twin and receiving characterization data for different nodes of the digital twin. A fingerprint is generated for the hierarchy based upon the characterization and a blueprint which corresponds to the fingerprint retrieved from a data store, the blueprint defining a set of parameters to be applied to different computing elements of different nodes in the hierarchy determined to minimize a cascading effect of a state changing condition in one of the nodes. Finally, the set of parameters of the retrieved blueprint is applied to corresponding computing elements of the digital twin and a state changing condition is simulated in the digital twin in a selected node so as to compute the cascading effect of the state changing condition to other nodes dependent upon the selected node.