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
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
- 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
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
- 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