Warehouse Route Selection Using Environmental Simulation
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Solution Overview
Problem
Customers and shoppers face challenges in determining efficient routes to obtain items in physical stores, due to complex store layouts, dynamic conditions such as item rearrangements, and obstacles, which hinder optimal time management.
Innovation Solution
The system generates and simulates different candidate routes for obtaining items at retailer premises, taking into account current or expected environmental conditions, and presents the most efficient routes to users to minimize time spent on shopping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If shoppers navigate stores without route optimization assistance, then they can shop independently, but they spend excessive time determining efficient routes and experience delays
Solution Approach 1:
The system pre-generates multiple candidate routes before the shopper begins shopping, simulating expected shopping behavior patterns to create optimized route options in advance. This eliminates the need for shoppers to spend time determining routes during their shopping trip.
Solution Approach 2:
The system acts as an intermediary between the store layout database and the shopper, providing route optimization assistance through a mobile device interface. This mediator translates complex spatial relationships into simple, actionable route recommendations for the user.
2Adaptability or versatility
If item locations are frequently rearranged in stores, then store layout flexibility improves, but route efficiency deteriorates due to dynamic changes
Solution Approach 1:
The system dynamically updates route recommendations based on current store layout data. When item locations are rearranged, the database is updated and subsequent route generations reflect these changes, ensuring shoppers receive optimized routes that account for the current configuration rather than a static layout.
Solution Approach 2:
The system incorporates feedback from actual shopping trips and environmental data to continuously improve route recommendations. When items are moved or layouts change, the system learns from updated data and adjusts future route suggestions to maintain efficiency despite dynamic changes.
3Measurement precision
If multiple candidate routes are generated and simulated, then route optimization accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the route generation process into distinct phases: generating multiple candidate routes based on shopping patterns, simulating each route to estimate difficulty and time, and selecting the optimal route. This segmentation allows complex computational tasks to be broken down into manageable steps that can be executed efficiently.
Solution Approach 2:
The system generates a limited number of candidate routes (e.g., top 3-5 options) rather than exhaustively evaluating all possible paths through the store. This partial action approach provides sufficient optimization accuracy while avoiding the computational burden of evaluating every conceivable route combination.
Data Source
AI summary
Different possible candidate routes for efficiently obtaining a set of items at given retailer premises are generated and simulated to estimate degrees of difficulty of the various routes, such as how long they are expected to take. The current conditions can be inferred based on analysis of environment data received from a plurality of devices associated with users shopping for items on the retailer premises, such as location data, camera data, or comments related to the retailer premises. The simulation takes into account current or expected conditions in the environment of the retailer premises, such as obstructions, alternative placements of items, etc. Routes with least degrees of difficulty may be presented to the users shopping for the items so that the users can use the most efficient routes when obtaining the items.