Enhancing Vessel Planning with Smart Stow
A major logistics provider felt the need to speed up container placement and maintain vessel balance. The manual approach wasn’t scaling, and delays were driving up costs. That’s when INTECH introduced an AI-powered reinforcement learning system. The solution automatically places 1000 containers in just 1.5 minutes, improving vessel stability and boosting crane utilization by 10%.
The client operates one of the busiest container logistics networks in the Middle East. With over 26000 active container locations and a constant flow of inbound and outbound vessels, their operations demand precision, speed, and faster decision-making.
They specialize in large-scale container yard management and are responsible for optimizing space, balancing vessel loads, and coordinating ground-side equipment such as cranes and trucks.
Their logistics environment is fast-paced, with every minute of inefficiency translating into added costs and scheduling conflicts.
When their traditional system no longer provided the flexibility or responsiveness their teams needed. They looked for a smart solution that could learn from past performance, adapt in real time, and scale without increasing operational overhead.
The client’s operations team faced a daily logistical challenge: How to place thousands of containers across limited space without compromising vessel stability and turnaround speed.
With demand surging and delivery windows shrinking, the pressure to optimize every decision was huge. Since the container placement relied heavily on manual planning and outdated rule engines, they couldn’t match the fast pace of logistics.
Here’s what the client’s team struggled with the most:
Unscalable Planning Models
With 26000 available locations, their existing system couldn’t compute optimal placements quickly.
Vessel Stability Risks
Misplaced containers led to load imbalances that threatened vessel safety and increased fuel consumption.
Crane Bottlenecks
Without visibility into ideal placement zones, cranes were overused in some areas and idle in others, creating inefficiencies and delays.
Multi-Objective Complexity
The client had to balance several objectives at one time, including safety, speed, cost, and equipment availability. The traditional system couldn’t handle all these factors together and quickly became ineffective.
High-Stakes Environment
Even one wrong step triggered cascading delays, missed container loading slots, and costly demurrage charges.
That’s when they approached INTECH to build a smarter solution to optimize container placement.
To bring order to the chaos of high-volume container placement, INTECH engineered a custom AI-Powered Reinforcement Learning (RL) System.
Here are the key features:
Reinforcement Learning Core
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INTECH used Deep Q-Network (DQN) as the heart of the solution. It’s an advanced reinforcement learning model that learns optimal container placement strategies through trial, feedback, and reward-based decision-making.
Multi-Objective Optimization
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The ML model was trained for multiple objectives, such as maximizing space, improving crane access, reducing container reshuffles, and ensuring vessel stability.
Dynamic Adaptation
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Unlike rule-based systems, this model kept learning. It adapted to seasonal shifts in container flow, crane availability, and vessel types, constantly refining its strategy to suit real-world conditions.
Yard Resource Optimization
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In addition to optimizing container placement, the solution also improved the efficiency of ground-side equipment usage, such as cranes and trucks. It aligned placement suggestions with available resources to prevent equipment bottlenecks.
Implementing an AI-driven reinforcement learning system in a live logistics environment demanded precision, collaboration, and trust.
INTECH partnered closely with the client’s yard planning and IT team to design an implementation roadmap that minimized disruption while maximizing long-term impact.
The process included these structured phases:
Reward Engineering and Model Training
INTECH began by building a training environment that simulated the client’s yard operations using historical container placement data. This allowed the model to “learn” by trial and error, guided by a carefully crafted reward system that reinforced good decisions and penalized inefficient ones.
Rewards were tied to operational priorities such as: Vessel stability, Crane accessibility, Spatial efficiency, Time-to-place, Cost of reshuffles
This foundational phase helped the model internalize the complex trade-offs planners deal with daily.
Integration with Live Yard Systems
Once the model reached a high-performance threshold in simulation, INTECH integrated it with the client’s existing systems using a lightweight Flask-based API. This allowed the AI to ingest real-time data from container yard management tools and return placement suggestions in sub-minute response time.
Real-World Testing and Iteration
INTECH launched a controlled pilot in one terminal to validate performance under real conditions. Planners reviewed AI recommendations alongside their own, providing live feedback that refined the model’s logic and reward structure. This feedback loop accelerated learning and made the system more aligned with the practical nuances of yard operations.
Full-Scale Rollout with Monitoring
After strong validation from the client’s team, INTECH rolled out the solution across all terminals. The system began generating real-time container placement plans, which yard planners could approve or adjust directly from an intuitive dashboard.
This step-by-step approach ensured that our AI solution became a supporting hand. It made smarter decisions without demanding any radical change from the operations team.
One month after implementing INTECH’s Reinforcement Learning System, here’s what changed:
The most valuable outcome wasn’t just speed or vessel stability; it was the efficiency our solution brought to everyday operations. Every container placement decision became guided, fast, and aligned.
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Python: Python drives the core AI and machine learning workflows. Its clean syntax and robust ecosystem allow rapid experimentation and reliable production deployment.
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PyTorch: We use PyTorch to build and train the Deep Q-Network model. Its dynamic computation graph and developer-friendly interface help us iterate quickly, run complex reinforcement learning simulations, and fine-tune performance in real-world conditions.
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Flask API: The Flask-based API enables seamless communication between the AI model and the client’s operational systems. It delivers sub-minute container placement recommendations based on live yard data, without disrupting existing workflows.
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Pandas and NumPy: These libraries handle the heavy lifting in data preparation. From feature engineering to outlier detection, they help ensure that only clean, high-quality data feeds the reinforcement learning engine.
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Simulation Engine (Custom Built): To train and test the model before deployment, we created a custom environment that simulates realistic yard scenarios. This sandbox lets us fine-tune the model’s reward logic without any risk to live operations.
