Idea
A model that predicts port congestion and optimizes berth scheduling to enhance shipment planning for port operators and logistics firms.
Research Paper
Core Innovation
This paper introduces Temporal-IRL, which applies Inverse Reinforcement Learning to infer berth scheduling priorities and vessel sequencing from historical AIS data. Unlike prior models, it reconstructs actual berth schedules and predicts vessel port stay times, enabling more accurate congestion forecasting. This approach improves operational decision-making at port terminals by learning from real-world vessel behavior.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: global port operations and logistics optimization market with growing demand for AI-driven scheduling solutions.
Potential Customers & Pain Points
- Port Operators Facing Inefficient Berth Scheduling
- Shipping Companies Experiencing Delays Due to Port Congestion
- Supply Chain Managers Needing Accurate Port Stay Time Forecasts
Business Model
SaaS platform offering berth scheduling and congestion forecasting tools with subscription tiers based on port size and data volume.
Competitive Landscape
- Navis
- StormGeo
- Portcall
Implementation Challenges
- Access to high-quality
- real-time AIS and port data
- Integration with existing port management systems
- Adoption resistance from traditional port operators
Validation Strategy
- Pilot deployment at Port of New York/New Jersey
- Compare predicted schedules with actual berth operations
- Gather user feedback from port operators and logistics managers
Research Paper Overview
Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning
Summary
This paper presents Temporal-IRL, a model that uses Inverse Reinforcement Learning to learn berth scheduling priorities and vessel sequencing at port terminals, specifically applied to the Port of New York/New Jersey. By analyzing historical AIS vessel position data, the model reconstructs berth schedules and predicts vessel port stay times, enabling accurate forecasting of port congestion to improve shipment planning and supply chain resilience.