Object Sorting Routing for False Side-by-Side Barcode Exceptions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sorting systems face inefficiencies and inaccuracies due to side-by-side classifications of objects, leading to unnecessary rescanning or manual intervention, particularly when multiple objects with common barcode information are scanned in close proximity.
Innovation Solution
A computer-implemented method and system that determines dimensional data for objects with common barcode information, identifies false side-by-side classifications, and controls routing based on comparing dimensional data to predetermined thresholds or user input, allowing objects to pass unimpeded when classifications are false.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If objects are scanned in close proximity (side-by-side), then scanning coverage is improved, but classification accuracy deteriorates due to false side-by-side exceptions
Solution Approach 1:
The system changes the parameter of dimensional data comparison by introducing characteristic dimensional thresholds associated with common barcode information. When an object's dimensional data falls within the characteristic range for its barcode, the system overrides the side-by-side classification and routes the object normally, thereby resolving false classifications while maintaining scanning coverage.
2Measurement precision
If all side-by-side classifications are processed with manual intervention, then routing accuracy is improved, but productivity deteriorates due to unnecessary manual handling
Solution Approach 1:
The system enables self-service by automatically determining whether side-by-side classifications are false exceptions using dimensional data comparison. Objects with common barcode information and matching dimensional characteristics are automatically routed without manual intervention, while only true exceptions require manual handling, thereby maintaining accuracy while improving throughput.
3Measurement precision
If dimensional data collection is implemented for all objects, then false classification identification is improved, but device complexity increases
Solution Approach 1:
The system applies universal dimensional thresholds associated with common barcode information to multiple objects of the same type. By creating a reusable dimensional profile for each barcode, the system efficiently identifies false exceptions across multiple objects without requiring individual complex analysis for each one, thereby reducing overall system complexity.
Data Source
AI summary
Some embodiments of the disclosure provide systems and methods for improving sorting and routing of objects, including in sorting systems. Characteristic dimensional data for one or more objects with common barcode information can be compared to dimensional data of another object with the common barcode information to evaluate a classification (e.g., a side-by-side exception) of the other object. In some cases, the evaluation can include identifying the classification as incorrect (e.g., as a false side-by-side exception).