INTECH developed a smart AI Yard Management System (YMS) for a logistics leader, to optimize their container placement and movement across 26,000+ locations.
Our solution streamlined client’s operations by reducing container shuffling and maximizing space utilization through intelligent location assignment algorithms.
About the Client
The client specializes in container management, overseeing large-scale loading and unloading operations at multiple facilities.
With over 26,000 container locations to manage, they required a smart system to enhance their operational efficiency and meet the demands of their dynamic logistics environment
Client’s Challenge
The client’s existing system relied on a rule-based approach, placing containers in available empty spaces without any optimization.
This led to challenges like:
- Inefficient utilization of container yard space.
- Increased container shuffling, resulting in wasted time and fuel.
- Inability to adapt to new rules or requirements for container placement.
To address these challenges, the client requested a solution capable of optimizing container placement while offering scalability and adaptability.
The Solution
Our team developed an AI-driven Yard Management System (YMS) using genetic programming to determine the most efficient container locations.
The system evaluated multiple parameters to optimize container placement and reduce shuffling, leading to significant improvements in operational efficiency.
Key Features
- Container Segregation: The Yard Management System (YMS) categorized containers based on their sizes (from 20ft to 40ft).
- Location Specification: Specific locations were designated for Import, Export, and Empty containers.
- Sequential Allocation: Prioritized container placement sequentially across location sets (e.g., A, B, C).
- Lower-Level Preference: Ensured the system must avoid suggesting locations that are at higher levels when there are empty slots available at lower levels.to minimize future shuffling.
- Scoring System: Used proximity, level, and distance scores to determine optimal placement.
Tools and Technologies Used
- Genetic Programming: Applied a genetic algorithm to evaluate and score all available locations, identifying the best options in real-time.
- Python: Developed the core AI and ML components using Python for efficient computation.
- Django Framework: Built a secure and maintainable interface for user interaction.
- Redis Database: Employed Redis for in-memory data storage, ensuring high-speed performance and scalability.
- REST API: Facilitated seamless communication between the system’s components.
Implementation Approach
The Yard Management System employs a multi-faceted scoring approach where each potential container location is evaluated based on three critical parameters:
- The Proximity Score evaluates locations based on their prefix designations, where locations marked with prefix ‘C’ are given preference over those with ‘D’. This strategic scoring ensures containers are placed in positions that minimize future movement requirements.
- The Level Score implements a vertical optimization strategy. The system recognizes that placing containers at higher levels (with Level 4 being superior to Level 3, and so on) provides significant operational advantages.
This arrangement allows for easier container shifting and reduces the need for extensive shuffling during retrieval operations. - The Distance Score calculates optimal placement by considering the container’s proximity to key operational areas.
Locations with prefix ‘A’ are positioned nearest to the gates, followed by ‘B’ locations at a moderate distance, while ‘C’ prefix locations are situated furthest from the primary access points.
This systematic approach ensures efficient grouping of containers based on their access requirements.
Business Impact
This smart yard management system led to the following operational improvements:
- Time Savings: Reduced container placement decision time.
- Cost Reduction: Decreased fuel consumption and equipment wear.
- Operational Flexibility: Enhanced ability to adapt to changing yard conditions.
- Efficiency Gains: Optimized container placement reducing shuffling operations.
- Resource Optimization: Improved equipment utilization through smarter movement planning.