This event-driven, microservice-based setup simulates real-time intermodal logistics and yard operations. It replicates the journey of freight containers through a transit facility—from gate arrival, to hostler parking, all the way to the final outgate—while tracking live operational metrics and ensuring data governance.
Supply chains often experience delays between actual yard movements and updates to enterprise databases, leading to issues such as dry runs, misparks, and inefficient capacity planning. Our project tackles this by leveraging cloud infrastructure and real-time data pipelines, giving dispatchers quick, actionable insights and securely logging employee actions.
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Cloud Database: AWS DynamoDB (NoSQL) for fast, key-value status updates.
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Simulation Engine: Python (
boto3) scripts that simulate gate clerks, yard hostlers, and outbound dispatchers. -
Analytics Front-End: Streamlit & Pandas for live data visualization and KPI monitoring.
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Live Ingestion Pipeline: Creates random inbound container traffic and assigns numeric parking spots to keep things running smoothly.
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Automated State Changes: Background processes update container statuses automatically (
Ingate_Hold→Parked→Departed). -
Terminal Appointment System (TAS): Smart business logic prevents "Dry Runs" by checking container availability before issuing gate codes.
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Internal Audit Trailing: Logs unique employee IDs linked to yard movements in the cloud, keeping audit data separate from the public dispatch dashboard.
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Real-Time Analytics: Continuously calculates and shows yard capacity usage and container dwell times.
- Clone the repo.
- Install dependencies with
pip install -r requirements.txt. - Set up your AWS CLI with the right IAM credentials (
aws configure) for DynamoDB access. - Start the dashboard using
streamlit run app.py. - Run the engine scripts (
gate_arrival.py,hostler.py, andoutgate.py) in separate terminals.
By implementing this cloud-native architecture, terminal operators can expect to see several key improvements:
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Elimination of Dry Runs: The Terminal Appointment System (TAS) successfully intercepts and denies gate access for units not physically grounded, saving drayage drivers hours of wasted time and reducing gate-lane congestion.
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Granular Accountability: Decoupling the public dispatch view from the internal AWS database ensures that every physical yard move is permanently tied to a specific hostler (e.g., EMP-309), providing management with an immutable audit trail for damage claims or misparks.
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Real-Time Capacity Visibility: Transitioning from batch-processed spreadsheets to an event-driven DynamoDB pipeline reduces visibility latency to near-zero, allowing dispatchers to accurately gauge yard utilization and average dwell times by the minute.
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