Multi-identifier Label System for Last-mile Delivery Routing
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Solution Overview
Problem
Conventional last-mile delivery routing systems are inefficient and prone to errors due to reliance on human interpretation of package labels, leading to confusion and delays in sorting, grouping, and delivery of packages.
Innovation Solution
A multi-identifier label system that uses a primary symbol to identify a group of packages for a specific driver and a secondary symbol to indicate the order and address of each package, with routes determined by a computer algorithm and labels printed for each package, eliminating the need for human error in sorting and delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional routing systems rely on human drivers to identify packages by name and address, then the system can operate with simple labeling, but human error increases and delivery accuracy decreases
Solution Approach 1:
The labeling system is segmented into multiple identifiers: primary symbols for driver assignment, secondary symbols for package sequencing, and alphanumeric codes for destination identification. This segmentation allows different components of the delivery process to be managed independently, improving reliability without overwhelming the driver with a single complex label
Solution Approach 2:
The patent introduces a computer algorithm as an intermediary between the routing system and the driver. The algorithm automatically generates optimized routes based on multiple parameters (traffic, weather, package priority) and translates them into simple symbolic instructions on labels, eliminating the need for drivers to interpret complex address information while maintaining system flexibility
2Measurement precision
If the system uses detailed alphanumeric labels with names and addresses, then package identification can be precise, but reading and interpretation errors increase with the number of deliveries
Solution Approach 1:
The patent employs color-coded symbols and printed indicators on package labels to encode delivery information. Different colors represent different delivery zones, priority levels, or driver assignments, allowing drivers to quickly distinguish packages without reading text. This visual encoding maintains precise identification while eliminating interpretation errors
Solution Approach 2:
The system transitions from one-dimensional text-based identification to two-dimensional symbolic representation on labels. Multiple pieces of information (driver assignment, sequence number, destination zone) are encoded in the spatial arrangement and visual characteristics of symbols rather than linear text, enabling rapid scanning and accurate identification even at high delivery volumes
3Productivity
If the system assigns multiple identifiers to each package, then sorting and grouping become more accurate, but the labeling and processing complexity increases
Solution Approach 1:
The system performs preliminary processing by pre-assigning primary symbols to drivers and pre-sequencing packages within each driver's route before delivery begins. This advance organization allows drivers to simply follow the predetermined symbolic sequence without making real-time decisions, improving sorting efficiency while the computer algorithm handles the complexity of generating these assignments
Solution Approach 2:
The multi-identifier labeling system is designed to be self-sorting: packages with the same primary symbol automatically group together for a specific driver, and secondary symbols automatically indicate the correct delivery sequence. The labels themselves contain all necessary information for automatic sorting without requiring additional manual processing or complex handling procedures
4Adaptability or versatility
If routes are determined by human judgment, then flexibility in handling special cases is maintained, but routing time and consistency decrease
Solution Approach 1:
The routing system is dynamic and adaptive, using computer algorithms that can process multiple variables (traffic conditions, weather, package priority, driver location) and recalculate optimal routes in real-time. This maintains flexibility to handle special cases while eliminating the time loss associated with human decision-making, as the algorithm instantly evaluates all parameters and generates optimized routes
Solution Approach 2:
The system incorporates feedback mechanisms where delivery data, traffic patterns, and route performance are continuously monitored and fed back into the routing algorithm. This allows the system to learn from past deliveries and automatically adjust future routes, maintaining adaptability to changing conditions while eliminating repetitive human judgment time through automated pattern recognition and optimization
Data Source
AI summary
Systems and methods for sorting, grouping, routing and delivering packages or products to end user customers comprises network-based routing and trip assignment. The packages or products are labeled with a using a plurality visual identifiers that are easily distinguished without requiring either the fulfillment staff or the delivery personnel to review the label for customer names or addresses.