AI Agentic Workflow for Port Congestion Analysis and Berth Allocation Optimization

Manual port surveillance, reliant on sporadic AIS checks and visual berth surveys, creates operational latency and suboptimal asset utilization. A custom AI agentic workflow automates this by ingesting real-time satellite imagery, computer vision models count waiting vessels and measure yard occupancy, and simulation agents model optimal berth assignments. This directly reduces demurrage costs, improves throughput, and provides a predictive edge over static scheduling, turning geospatial data into a continuous operational lever.

Implementation integrates this orchestration layer with the Port Community System (PCS), Terminal Operating System (TOS), and ERP platforms like SAP. Controls include confidence scoring on vessel counts, exception routing for ambiguous allocations to a human dashboard, and full audit trails for allocation decisions. The architecture is built for continuous retraining of vision models on new satellite sources and scales to monitor multiple terminals, providing a production-grade system that replaces guesswork with automated, evidence-based resource commands.

This workflow automates the manual, daily bottleneck of analyzing satellite imagery and AIS data to assess port congestion and manually assign berths. It replaces hours of analyst work with minutes of automated intelligence, directly reducing vessel turnaround times, minimizing demurrage costs, and improving terminal asset utilization. The operational upside comes from continuous, data-driven decision-making that reacts to live conditions—waiting vessel counts, yard occupancy, and weather delays—rather than static schedules.

Implementation centers on a LangGraph-based orchestrator that sequences specialized agents for computer vision, simulation, and optimization. The Vessel Detection Agent processes optical and SAR imagery via a containerized model. The Optimization Engine, using constraint programming, evaluates berth assignments against business rules. The final allocation is routed through a configurable approval gate before pushing work orders to systems like Navis or SAP TM. Critical controls include confidence scoring for detections, human review for low-confidence or high-risk allocations, and full audit trails for maritime compliance.

Phase 1 establishes the core data pipeline, ingesting daily satellite imagery from Planet or Sentinel-2 and AIS feeds into a cloud data lake. We deploy initial computer vision agents for vessel detection and container yard occupancy mapping, outputting raw analytics to a staging database. This foundational 8-week sprint delivers the first automated congestion dashboard, providing immediate visibility while isolating integration risk. The focus is on data quality validation, model accuracy benchmarking, and establishing the observability layer for all downstream agents.

Phase 2 builds the LangGraph-based orchestration core, where specialized agents for track correlation, ETA prediction, and berth simulation are wired together with approval gates. The optimization engine, using historical turnaround data, runs what-if scenarios to recommend allocations. Concurrently, Phase 3 integrates this logic with the Port Community System and Terminal Operating System via secure APIs, embedding recommended berth plans into operator workbenches. A final pilot phase validates the full loop—from satellite trigger to allocated berth in the TOS—before enterprise rollout, ensuring operational buy-in and measurable ROI on vessel turnaround time.

Effective automation hinges on integrating disparate, often messy data streams. The workflow must ingest and normalize AIS vessel tracks, high-resolution satellite imagery from providers like Planet or Airbus, terminal operating system (TOS) berth schedules, and terrestrial IoT sensor data. Data quality pipelines are critical to handle missing signals, correct geolocation errors, and synchronize timestamps, forming a clean, unified situational picture for downstream models and agents.

Integration is where value is captured or lost. The decision layer must push optimized berth assignments and congestion alerts directly into the Port Community System (PCS) or TOS via robust APIs, triggering real-world resource allocation. Concurrently, a human-in-the-loop dashboard, built on tools like Grafana or custom React, provides controllers with override capability, model confidence scores, and exception routing. This closed-loop architecture, monitored for data drift and API health, turns analysis into operational leverage, reducing vessel turnaround time and demurrage costs.

A governed deployment starts with a human-in-the-loop approval gate for all berth allocation recommendations before they are sent to the Terminal Operating System (TOS) or Port Community System (PCS). This control ensures operational safety and allows for expert validation of AI-driven logic, especially during initial learning phases. All agent decisions, data inputs from satellite providers like Planet or Airbus, and human overrides are logged to an immutable audit trail, providing full traceability for performance review and regulatory inquiries.

Rollout should be phased, starting with a single terminal as a pilot to monitor model accuracy and integration stability with systems like Navis or SAP TM. Phase two expands to all berths within the port, while phase three integrates predictive congestion alerts and upstream coordination with feeder vessels and rail operators. Each phase includes defined KPIs for throughput improvement and demurrage reduction, with regular governance reviews to adjust confidence thresholds and agent behavior before scaling the autonomous workflow across the entire port network.