Dynamic Slotting Digital Twin for Warehouse Congestion
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Solution Overview
Problem
Current simulation and warehouse management software fail to optimize re-slotting and picking processes in complexes, leading to congestion, resource waste, and inefficient use of storage space due to inadequate consideration of item nesting and entity behavior.
Innovation Solution
A facility using a digital twin simulation model to dynamically control re-slotting and picking processes by determining optimal storage locations and pick sequences, taking into account item attributes, congestion factors, and entity efficiency to reduce congestion and improve resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If items are re-slotted to high-velocity storage locations near the staging area, then picking speed is improved, but congestion increases and entities experience more idle time
Solution Approach 1:
The system dynamically determines storage locations and pick sequences based on real-time simulation of entity movements and congestion factors, rather than using static high-velocity location assignments. This allows the system to adapt to changing conditions and avoid releasing multiple entities to the same congested locations simultaneously.
Solution Approach 2:
The system performs preliminary simulation and analysis to identify optimal storage locations and pick sequences before entities are released. By predicting congestion patterns and entity behaviors in advance, the system can prevent congestion before it occurs, rather than reacting to it afterward.
2Productivity
If multiple entities are released simultaneously with similar pick sequences, then order fulfillment throughput is improved, but congestion increases in storage areas
Solution Approach 1:
The system assigns different pick sequences to different entities based on their specific characteristics and the current state of the complex. Instead of giving all entities the same pick sequence, each entity receives a customized sequence that avoids congested areas, allowing parallel operations without conflict.
Solution Approach 2:
The system uses simulation feedback about entity behaviors, congestion patterns, and storage area states to continuously adjust pick sequences and release decisions. This feedback loop allows the system to maintain high throughput while avoiding congestion by adapting to real-time conditions.
3Quantity of substance
If item nesting is not properly accounted for in storage, then storage density appears higher, but actual usable storage capacity is reduced
Solution Approach 1:
The system properly accounts for item nesting by simulating how items physically nest within each other in storage locations. This allows accurate calculation of usable storage capacity and ensures that nested items are correctly positioned and accessible, maximizing actual storage efficiency.
Data Source
AI summary
A facility for determining re-slotting items and dispatching entities for picking operations is described. At a first time, the facility receives an indication of orders that indicate a time at which at least one item is scheduled to depart from a complex. The facility generates pick sequences from the orders and obtains simulation data that includes predicted states of the complex by applying the pick sequences to a digital twin simulation of a complex. The facility identified alternative storage locations for items included in the pick sequences based on the predicted states of the complex. At a second time after the first time, the facility receives data indicating a current state of the complex and identifies a pick sequence based on the alternative storage locations, current state of the complex, and the generated pick sequences. The facility dispatches entities to execute the identified pick sequence.