Transforming Container Operations with AI-Powered Yard Management System
A logistics company struggled to manage thousands of container slots across multiple yards. Their containers were loaded inefficiently, burning more fuel and exhausting operational teams with constant re-shuffling. INTECH helped the client with an AI-powered Yard Management System that made every container placement smarter, faster, and easier to scale.
The client operates high-volume container yards that form the backbone of its logistics network. With over 26,000 active container locations across multiple sites, each placement decision directly affects operational speed, cost-efficiency, and yard productivity.
As the volume of containers increased, so did the complexity. Misplaced containers, repeated shuffling, and avoidable delays became daily hurdles. Teams spent valuable hours fixing preventable errors instead of focusing on throughput and optimization.
In an industry where every minute counts, manual yard management is not sustainable.
Containers were placed wherever space was available. The client had no system to predict which container needs to move first or where it should go for minimal disruption later.
As a result, supervisors and yard workers were stuck in a constant loop:
Re-shuffling containers multiple times a day
Teams moved the same containers frequently to access the ones buried underneath.
Wasting fuel and machine hours
Each adjustment burned diesel and added unnecessary wear on handling equipment.
Manually scanning for available slots
Staff walked or drove around the yard to find open spaces that fit updated delivery needs.
Delaying critical calls
With no dynamic system in place, teams couldn’t quickly re-prioritize containers when schedules changed.
When efficiency dropped and expenses climbed, the client called on INTECH to find a better way.
INTECH designed a smart Yard Management System that replaced rule-based container management with intelligent placement mapping.
With AI and genetic programming, the new system studied every inch of the yard, including location types, access levels, and container categories. This helped the client’s operations team make precise placement decisions in real time.
Here are the key features:
Container Grouping by Type and Size
-
The system automatically classified containers into import, export, and empty categories. It also separated them based on size (from 20ft to 40ft) to ensure better stacking and faster retrieval.
Lower-Level Prioritization
-
Instead of stacking containers at random, the system looked for empty spots on lower levels first. This reduced the need for future re-shuffling and saved handling effort.
Sequential Placement by Yard Zones
-
We introduced a structured approach by dividing the yard into clear zones: A, B, and C.
-
Now, containers were placed in a planned sequence. This eliminated the need to move back and forth across the yard, saving time and reducing confusion during daily operations.
Proximity and Distance Scoring
-
Every possible slot was scored based on:
-
- How close it was to gate access (faster exit for priority shipments)
- How easy it was to reach without disturbing others
- The floor level for easier machine access
-
With these smart features in place, INTECH focused on turning this foundation into a fully functional system.
INTECH followed a collaborative, agile implementation process that balanced speed with precision. Every stage was grounded in real operational behavior and yard dynamics.
Here is how we did it:
Defining Scoring Logic and Yard Zones
First, we worked with the operations team to define yard zones and container behavior patterns. We created a multi-score logic based on three key signals: proximity, level, and access distance, to guide container placement decisions.
Training and Testing the Scoring Model
We fed historical yard movement data into the AI model to test the scoring logic in real-world conditions. We also simulated multiple placements and retrievals to reduce interference and improve container accessibility.
System Integration and Pilot Rollout
We integrated the YMS into the client’s existing logistics platform using REST APIs. During pilot testing, we deployed the scoring engine across two representative yards.
Full Deployment and Continuous Optimization
Once the pilot showed measurable improvements in shuffling, time savings, and machine routing, we scaled the system to all 26000 container locations. We added Redis to handle high-volume queries with sub-second response times, ensuring real-time placement suggestions under peak load.
Within the first month, yard teams reported smoother handoffs, fewer last-minute movements, and more predictable workflows.
The impact of our solution includes:
The Smart Yard Management System didn’t just streamline container operations; it empowered teams with intelligent automation and actionable insights.
This eliminates guesswork and enables precise control over yard logistics.
-
Genetic Programming: Powered the optimization engine behind intelligent container placement by simulating thousands of placement strategies and selecting the best outcomes based on evolving yard conditions.
-
Python: Formed the backbone of the system's logic and algorithm development, enabling rapid prototyping and customization.
-
Django Framework: Used for building a robust backend to manage data flow, user interfaces, and administrative controls within a scalable architecture.
-
Redis Database: Provided high-speed data access for real-time decision-making, allowing the system to quickly respond to changing yard states.
-
REST API: Ensured smooth integration with existing TMS, WMS, and yard equipment systems, facilitating real-time updates and centralized control.
