Dynamic Delivery Incentive Optimization via Machine Learning
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Solution Overview
Problem
Current systems for determining delivery associate incentives in on-demand delivery platforms are inefficient and prone to human error, struggling to balance delivery quality with budget constraints, especially during demand peaks.
Innovation Solution
A method using machine learning algorithms to predict demand and supply, combined with integer programming, to optimize incentive values for delivery associates, ensuring adequate delivery quality while staying within budget, by generating predicted demand and supply values based on historical data and dynamically updating them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to determine incentive values, then human administrators can examine historical data and make decisions, but the process is inefficient and prone to human error
Solution Approach 1:
The patent replaces the manual mechanical process of examining historical data and making educated guesses with an automated computer-based system that uses machine learning algorithms and integer programming to calculate optimal incentive values, thereby eliminating human error and significantly reducing the time required
Solution Approach 2:
The system enables self-service by automatically generating incentive value recommendations without requiring human administrators to manually analyze data, as the computer system independently processes historical data, predicts demand and supply, and determines optimal incentive values through algorithmic optimization
2Reliability
If bonus incentives are increased to ensure adequate supply of delivery associates during demand peaks, then delivery quality is maintained, but the cost per delivery increases significantly
Solution Approach 1:
The patent changes the parameter of incentive values from fixed or manually-set amounts to dynamically optimized values determined by integer programming algorithms that consider multiple constraints including budget limits, delivery quality requirements, and predicted demand-supply balance, enabling precise control over both quality and cost
Solution Approach 2:
The system introduces dynamics by making incentive values adjustable and optimizable based on varying conditions such as predicted demand, predicted supply, budget constraints, and delivery quality thresholds, allowing the platform to adapt incentive levels to different scenarios rather than using static incentive structures
3Quantity of substance
If incentive values are set high to attract delivery associates, then supply increases, but it becomes difficult to stay within budget
Solution Approach 1:
The patent applies partial action by determining incentive values that are sufficient to achieve the desired supply level without providing excessive incentives, using integer programming to find the optimal balance point that meets minimum supply requirements while minimizing budget consumption
Solution Approach 2:
The system incorporates feedback mechanisms by using predicted supply data in the integer programming optimization, where the relationship between incentive values and resulting supply levels is considered, allowing the system to adjust incentive values based on expected supply outcomes and iterate toward optimal solutions that satisfy both supply and budget constraints
Data Source
AI summary
Provided are various mechanisms and processes for generating delivery associate incentive values. In some implementations, predicted demand can be generated based on a first set of historical data and predicted supply can be generated based on a second set of historical data. Delivery quality values can be generated based on the predicted demand and the predicted supply. The delivery quality values can be used to determine incentive values that are provided to delivery associates of an on-demand delivery platform.