Machine Learning Wait-Time Prediction for Nearby Order Locations
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Solution Overview
Problem
Shoppers in online concierge systems often face long wait times for desirable orders and are unaware of better order opportunities at nearby locations, leading to inefficient order acceptance and location choices.
Innovation Solution
A system uses a machine learning model to predict wait times based on current location, order volume, available shoppers, and historical data, suggesting alternative locations with shorter total wait times by considering travel time, and adjusts predictions based on actual wait time feedback to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If shoppers wait for more desirable orders at their current location, then order quality improves, but wait time increases
Solution Approach 1:
The system performs preliminary actions by predicting wait times for multiple locations before the shopper makes a decision. The machine learning model calculates expected wait times for various locations in advance, allowing the shopper to proactively choose the optimal location rather than passively waiting at the current location.
Solution Approach 2:
The system implements dynamics by continuously updating wait time predictions as new orders become available and as shopper locations change. The predictions are dynamic and adapt to real-time conditions, allowing the system to provide current, accurate recommendations rather than static information.
2Ease of operation
If shoppers are restricted to their current geographical area, then location simplicity is maintained, but order opportunities are limited
Solution Approach 1:
The system adds a spatial dimension to the order acceptance decision by considering multiple geographical locations. Instead of only evaluating orders at the current location, the system extends the search space to nearby locations, allowing shoppers to access a broader range of order opportunities while maintaining geographical proximity.
Solution Approach 2:
The machine learning model acts as an intermediary that bridges the shopper and multiple locations. The model processes information about orders at various locations and presents synthesized wait time predictions, simplifying the complex task of evaluating multiple locations into a single comparative metric for the shopper.
3Measurement precision
If the system provides detailed wait time predictions for multiple locations, then decision accuracy improves, but system complexity increases
Solution Approach 1:
The system uses copying by creating simplified representations of complex location data through machine learning models. Instead of presenting raw data about orders, shoppers, and locations, the system generates copied wait time predictions that capture the essential information needed for decision-making in a simplified, comparable format.
Solution Approach 2:
The system applies parameter changes by transforming multiple input parameters (order availability, shopper locations, historical data) into a single output parameter (predicted wait time). This parameter transformation simplifies the decision-making process while maintaining prediction accuracy across multiple locations.
Data Source
AI summary
Techniques for predicting a wait time for a shopper based on a location the shopper's client device are presented. A system identifies a shopper's current location and uses a machine learning model to predict a wait time until the shopper will receive one or more orders. The machine learning model is trained to use input features including a number of orders received during a current time period for fulfillment near the current location, a number of other shoppers available for fulfilling orders during the current time period near the current location, historical information about a presentation of a plurality of orders to a plurality of shoppers near the current location, and historical information about the shopper and the other nearby available shoppers. The system then sends the predicted wait time to the client device for presentation to the shopper.