Dynamic Shopping Route Optimization Using Real-Time Location Data
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Solution Overview
Problem
Consumers face inefficiencies when shopping in stores with multiple aisles, as they often need to cross the store multiple times to find items on their list, and existing digital shopping lists do not effectively account for varying store layouts or real-time changes in product locations.
Innovation Solution
A method that links items on a shopping list to locations within a store, using a dynamic system to analyze and optimize the shopping route by building an undirected graph of locations and calculating probabilities of item locations, allowing for real-time updates and personalized routes without pre-defined area information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consumers use a traditional shopping list to organize items, then they can remember what to buy, but they still need to cross the store multiple times to find all items
Solution Approach 1:
The system dynamically generates optimized routes based on real-time store layout data and item locations. Instead of using a static shopping list, the system continuously adapts the recommended path as users move through the store and as items are purchased, transforming the rigid list approach into a flexible navigation system that minimizes traversal time.
Solution Approach 2:
The system introduces a digital intermediary (mobile application) that acts as a mediator between the shopping list and the physical store environment. This intermediary processes item locations, store layout, and user progress to generate optimized routes, replacing the direct but inefficient approach of manually searching for each item on a traditional list.
2Loss of time
If consumers manually organize items by store section, then they can reduce some crossings, but this requires significant effort and may not adapt to layout changes
Solution Approach 1:
The system performs automatic route optimization without requiring manual organization by the user. The application autonomously processes store layout data, item locations, and purchasing information to generate optimized routes, eliminating the need for consumers to manually categorize items by section while still achieving efficient traversal.
Solution Approach 2:
The system pre-processes store layout information and item locations before the shopping trip begins. By having the route optimization data prepared in advance based on known store configurations and item placements, the system eliminates the need for real-time manual organization during the shopping experience.
3Productivity
If the system uses pre-defined area information to optimize routes, then routing can be efficient, but the system cannot adapt to real-time changes in product locations or store layouts
Solution Approach 1:
The system transitions from static pre-defined area information to dynamic real-time data processing. It continuously updates route recommendations based on current store conditions, item locations, and user progress, allowing the routing system to adapt to layout changes while maintaining optimization efficiency through automated recalculation.
Solution Approach 2:
The system implements feedback loops that monitor user progress through the store and detect changes in item availability or location. This feedback information is fed back into the route optimization algorithm, which then adjusts the recommended path accordingly, enabling adaptation to real-time changes while maintaining efficient routing.
4Ease of operation
If multiple users access and modify the same list simultaneously, then collaboration is improved, but the sorting and optimization becomes more complex
Solution Approach 1:
The system uses feedback mechanisms to detect and resolve conflicts from simultaneous user modifications. When multiple users add, remove, or mark items, the system continuously monitors changes and dynamically recalculates the optimized route based on the current state of the shared list, maintaining collaboration while managing complexity through automated conflict resolution.
Solution Approach 2:
The route optimization system dynamically adapts to real-time changes in the shared shopping list caused by multiple users. Instead of requiring fixed organization before use, the system continuously recalculates the optimal route as items are added, removed, or marked as purchased by any user, making the collaboration feature work seamlessly with automatic re-optimization.
Data Source
AI summary
The present invention elates to methods and devices for optimizing a route based on an electronic list, the method comprising the steps of: linking items in a list of items to a position in an area and providing a sorted optimized visiting order wherein the optimization is based on historical data comprising prior visited positions.