Study Case
Optimise the routing of containers from the Container terminal yard or Depo area to different Container Freight Stations (CFS) within the port, where the cargo is stuffed/stripped, and vice-versa.
This is one of the most important operations in the port in terms of reduced routing times, lower CO2 emissions, higher truck/forklift utilization, and service level agreements (e.g., times of delivery, compliance with goods sensitivity, etc.).
Demonstration
![port](/sites/nephele/files/public/styles/large_800/public/content-images/media/2024/port-4x.png?itok=g7RNUYvN)
Simulation of implementation in Port of Koper (Luka Koper) in Simulation Platform SUMO (Simulation of Urban Mobility) Programmability features:
- Simulation Controller (Traci) Python interface to SUMO
- Forwarding updated truck position information and flow demands to Agent.
- Generation of Time Sensitive Schedules
- TAPRIO (Time-Aware Priority; one open source implementation, part of the synchronous Time Sensitive Networking standards, developed by Intel for the IEEE 802.1Qbv) GCL (Gate Control List) updated
Scenarios
![containers](/sites/nephele/files/public/styles/large_800/public/content-images/media/2024/container-4x.png?itok=CfJlcgY3)
- Trucks transport containers
- VO responsible for truck IoTs at the edge.
- Truck relocation results change of flow demands.
- Change in the flow requirements as Truck relocate.
- New GCL (Gate Control List) has to be computed to satisfy changes along with previous flow requirements.
Goal
![road](/sites/nephele/files/public/styles/large_800/public/content-images/media/2024/road-4x_0.png?itok=4gbS7wFE)
The goal is Container routing optimization. The application will propose optimal path/track for routing each container within the port area according to requirements given by freight forwarder and to temporal conditions in the port.
Description
- Data collection application component collects data from sensors and cameras installed/mounted in the field and provides them in suitable format to the other components.
- Object detection is an AI/ML algorithm responsible for detection of free parking lots needed for containers unloading.
- Logistic Scheduling Agent collects data from sensors, object detection algorithm and data from external sources (Port Information System) to provide schedule and optimal routes for containers routing
- Real-time adaptive logistics scheduling Agent
- Backlog API handling freight data from terminal’s ERP
- Traffic delays API receiving real-time traffic data
- Dispatch Decision Making is a final application component which provides Logistic Scheduling Agent’s outcome to the port staff in order to execute container routing.
Technical constrain
![technical](/sites/nephele/files/public/styles/large_800/public/content-images/media/2024/technical-4x_0.png?itok=uI01XPLv)
- Software components orchestration
- Device Management
- Interfaces to IoT devices/sensors and to data, relevant for the business process (Port Information System)
- Sensor data collection and aggregation
- AI/ML supported computer vision for information extraction (object detection)
- AI/ML supported containers routing optimization algorithm
- Providing feedback to port personnel and freight forwarders
- 5G network coverage