Order Processing Sequence Optimization for Logistics Picking Efficiency
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Solution Overview
Problem
Conventional rack transfer systems in logistics are not optimized to handle large numbers of orders that can be rearranged in processing sequence, leading to inefficiencies in simultaneous picking ratios and overall workflow.
Innovation Solution
An information processing device determines the processing sequence of orders and assigns racks based on rack data, using hierarchical clustering to merge similar orders into clusters, ensuring that the number of clusters matches or exceeds the number of work stations, thereby optimizing the simultaneous picking ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If orders are processed sequentially in real-time as they are added to the system, then the system operation is simple, but the simultaneous picking ratio is low and overall efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by determining the processing sequence of multiple orders before actual picking operations begin. The sequence determination unit analyzes all incoming orders and pre-establishes an optimized processing sequence that maximizes simultaneous picking opportunities, rather than reacting to orders as they arrive sequentially.
Solution Approach 2:
The order processing system is segmented into distinct functional units: the sequence determination unit that optimizes the processing order, the rack assignment unit that assigns specific racks to orders, and the execution units at workstations. This segmentation allows each unit to specialize in its function, improving overall simultaneous picking ratio without overwhelming the system.
2Productivity
If the processing sequence of orders is rearranged to improve simultaneous picking ratio, then productivity improves, but the complexity of determining optimal sequences increases
Solution Approach 1:
The sequence determination unit acts as an intermediary between order reception and rack assignment. It receives order information, determines an optimized processing sequence based on product data and workstation capabilities, and passes this sequence to the rack assignment unit. This intermediary layer simplifies the overall system by centralizing the complex sequencing logic in one dedicated component.
Solution Approach 2:
The system performs preliminary sequence determination by analyzing all incoming orders before execution. By pre-calculating the optimal processing sequence based on product locations, workstation availability, and order requirements, the system avoids complex real-time decisions during actual picking operations.
3Productivity
If hierarchical clustering is used to merge similar orders into clusters, then rack assignment efficiency improves, but the computational processing time increases
Solution Approach 1:
The order set is segmented into multiple clusters based on similarity in product requirements and rack assignments. Each cluster represents a group of orders that can be efficiently processed together using the same rack sequences. This segmentation reduces the computational complexity of rack assignment by breaking down large-order problems into smaller, manageable clusters.
Solution Approach 2:
Similar orders are merged into clusters that share common rack assignment patterns. By combining orders with similar requirements into single clusters, the system reduces redundant rack movements and assignments, improving overall rack assignment efficiency while managing computational load through pattern recognition.
Data Source
AI summary
According to an embodiment, an information processing device includes processors configured to: determine, based on a plurality of pieces of rack data including first identification information of one or more kinds of products housed in each rack, a processing sequence of a plurality of pieces of first order data including second identification information of one or more kinds of products to be picked, and one or more racks from which the products identified by the second identification information are to be picked; and perform hierarchical clustering that repeats processing of merging similar or matching pieces of first order data into a cluster such that a cluster number of a plurality of clusters resulting from the hierarchical clustering becomes equal to or more than a station number of a plurality of work stations where housing containers corresponding to at least some pieces of first order data are placed.