INTECH developed a smart AI Yard Management System (YMS) for a logistics leader, to optimize their container placement and movement across 26,000+ locations.
The solution streamlined client’s operations by reducing container shuffling and maximizing space utilization through intelligent location assignment algorithms.
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
The existing system relied on a rule-based approach, placing containers in available empty spaces without any optimization.
This led to challenges like:
The client roped in INTECH to evolve a solution capable of optimizing container placement while offering scalability and adaptability.
INTECH 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 that not only reduced shuffling but also improved operational efficiency.
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.
The Yard Management System employs a multi-faceted scoring approach where each potential container location is evaluated based on three critical parameters:
1. 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.
2. 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.
3. 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.
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.
Genetic Programming
Python
Django Framework
Redis Database
REST API