Robot Picking Zone Allocation for Simultaneous Order Fulfillment
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Solution Overview
Problem
Conventional logistics centers face inefficiencies in robot task allocation, leading to longer worker movement lines and reduced effectiveness due to non-optimal assignment of robots for loading shipments.
Innovation Solution
A server-based method that identifies orders, picks zones, and allocates robots based on delivery item quantity, storage location, and picking station characteristics to optimize robot utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robots are assigned tasks without optimal allocation to picking zones, then robot task completion is achieved, but the continuity of workers is reduced and the worker moving line becomes longer
Solution Approach 1:
The system segments the warehouse into multiple picking zones based on storage locations and assigns robots to specific zones. This segmentation allows robots to focus on dedicated areas, reducing unnecessary movement and improving picking efficiency while minimizing the worker moving line length.
Solution Approach 2:
The server pre-calculates optimal robot assignments to picking zones before tasks begin, considering factors like storage location distribution and robot positions. This preliminary allocation ensures continuity of workers and optimizes the moving line before operations start.
2Productivity
If robots are assigned tasks without optimal allocation to picking zones, then task completion is achieved, but effectiveness is reduced
Solution Approach 1:
The system dynamically adjusts robot assignment parameters based on real-time conditions, including the number of picking stations in each zone, quantity of delivery items, and robot positions. This parameter optimization ensures both high productivity and reliable effectiveness by matching robots to zones with the most appropriate characteristics.
Solution Approach 2:
The server continuously monitors robot states and picking zone conditions, using this feedback to optimize and reassign robots to picking zones dynamically. This feedback mechanism ensures that effectiveness is maintained by adjusting allocations based on actual operational conditions.
3Productivity
If multiple orders are processed simultaneously without optimized zone allocation, then order fulfillment is achieved, but picking efficiency is reduced
Solution Approach 1:
The system segments multiple orders into groups associated with specific picking zones, allowing robots to process orders within designated areas. This segmentation simplifies the complexity of handling multiple simultaneous orders by organizing them into zone-based task groups, improving picking efficiency while managing complexity.
Solution Approach 2:
The server implements a universal task allocation mechanism that can handle multiple orders simultaneously by assigning them to appropriate picking zones based on common characteristics like storage location and item quantity. This multi-functional approach processes diverse orders through a unified allocation system.
Data Source
AI summary
A method of operating a server is disclosed. The operation method of the disclosure, includes: identifying, by an product management module, a plurality of orders including a quantity of delivery item and a storage location of the delivery item; identifying, by a destination management module, a plurality of picking zones for loading each delivery item based on the storage location of the delivery item included in each of the plurality of orders; and selecting, by a task allocation module, at least one picking zone among the plurality of picking zones based on at least one of the quantity of delivery item matched to each of the plurality of picking zones, the number of picking stations included in each of the plurality of picking zones, and the location of the picking stations included in each of the plurality of picking zones.