Distance Prediction Model for Concierge Logistics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional online concierge systems face inaccuracies in determining travel distances for shoppers due to the limitations of Haversine distance calculations, which do not account for geographical features like street layouts and directions, and incur high computational costs when using more accurate predictive models.
Innovation Solution
The online concierge system generates and trains a distance prediction model using historical data on actual travel distances, incorporating Haversine distances as input, to provide more accurate estimates of travel distances between locations, thereby reducing computational resources and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If Haversine distance is used to determine travel distance, then computational resources are conserved, but accuracy of distance determination deteriorates
Solution Approach 1:
The system pre-calculates and stores travel distances between pairs of locations in a distance matrix before they are needed for order assignment. This preliminary computation allows the system to quickly retrieve pre-computed distances during runtime without performing expensive real-time calculations, thus maintaining high accuracy while reducing operational computational costs.
Solution Approach 2:
Instead of using simple Haversine distance calculations, the system creates a comprehensive distance matrix that copies and stores actual travel distances between all pairs of locations. This copied distance information, derived from detailed route calculations or GPS data, replaces the need for repeated complex computations and provides accurate distance values for assignment algorithms.
2Measurement precision
If a detailed predictive model is used to increase accuracy of shopper distance, then measurement precision improves, but computational resources and computation time increase
Solution Approach 1:
The system performs detailed distance calculations in advance and stores results in a distance matrix. This preliminary action shifts the computational burden from runtime to setup time, allowing accurate distance measurements to be retrieved efficiently during order assignment without incurring high computational costs at the moment of decision-making.
Solution Approach 2:
The system computes distances for all possible pairs of locations (excessive action) rather than calculating distances only when needed for specific assignments. This comprehensive pre-computation ensures that accurate distance data is available for any location pair that may arise during order fulfillment, while the actual runtime complexity is reduced to simple matrix lookups.
3Measurement precision
If a third party system is queried to generate distances between two points, then measurement precision improves, but processing time and computational resources increase
Solution Approach 1:
The system creates a local copy of distance information in a pre-computed distance matrix, eliminating the need to query external third-party systems during runtime. This copied distance data is stored locally and can be retrieved instantly, providing both high accuracy and fast processing without external dependencies.
Solution Approach 2:
The system performs distance calculations in advance and stores them in a distance matrix, so that when order assignments need to be made, the distances are already available. This preliminary computation replaces time-consuming real-time queries to third-party systems with fast local data retrieval.
Data Source
AI summary
An online concierge system receives orders from users and assigns orders to shoppers for fulfillment. Each order specifies a destination location and a warehouse from which items in the order are obtained. When assigning orders to shoppers, the online concierge system seeks to minimize distances traveled by shoppers fulfilling orders. To more efficiently assign orders to shoppers, the online concierge system trains a distance prediction model to predict a distance traveled between a starting location and a destination location from the starting location, the destination location, and a Haversine distance between the destination location and the starting location. Information identifying distances traveled by shoppers when fulfilling previous orders or information about distances between locations from a third party system may be used to train the distance prediction model.