Ranked Surge Pricing Offers for Budget-Constrained Picker Supply
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Solution Overview
Problem
Existing online concierge systems face challenges in effectively managing supply and demand for shoppers (pickers) while adhering to budget constraints, as surge pricing models often fail to optimize compensation strategies during varying demand periods.
Innovation Solution
An online concierge system employs a surge pricing model that iteratively simulates wages using supply and demand metrics, optimizing multipliers through hyperparameter adjustments to maintain budget adherence and enhance picker participation, utilizing machine learning to predict user acceptance likelihoods and present ranked opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If surge pricing is implemented to encourage more shoppers during high-demand periods, then picker participation increases, but the cost to the system increases
Solution Approach 1:
The system implements dynamic surge pricing multipliers that automatically adjust wage offers based on real-time supply and demand conditions. The multiplier varies over time and location, increasing during high-demand periods to attract pickers and decreasing during low-demand periods to control costs, creating a dynamic compensation strategy rather than a static one
Solution Approach 2:
The system changes the wage parameter by applying surge multipliers to base wages. The multiplier value is adjusted based on forecasted supply and demand metrics, allowing the system to optimize picker participation while managing overall wage expenditure through parameter modification rather than structural change
2Quantity of substance
If higher wages are offered to increase picker supply, then more pickers are available, but budget constraints are violated
Solution Approach 1:
The system employs feedback mechanisms where wage simulations are performed iteratively to evaluate the impact of different surge pricing strategies on both picker supply and budget adherence. The system learns from simulation results and adjusts surge multiplier strategies to achieve budget neutrality while maximizing picker availability
Solution Approach 2:
The system performs wage simulations in advance to predict the impact of surge pricing strategies before implementing them. By simulating different scenarios and evaluating their effects on budget and supply, the system can pre-determine optimal surge strategies that guarantee budget adherence while achieving desired picker supply levels
3Ease of operation
If surge pricing is applied uniformly across all time periods, then implementation is simple, but it fails to optimize wages during varying demand periods
Solution Approach 1:
The system segments the pricing strategy by time period, location, and demand conditions rather than applying a uniform multiplier. Different surge multipliers are assigned to different time windows based on forecasted supply and demand metrics, allowing optimized wage offers for each segment while maintaining manageable complexity through automated segmentation
Data Source
AI summary
An online concierge system schedules pickers (shoppers) to fulfill orders from users. During periods of peak demand, the system increases compensation to shoppers to encourage more to participate, thereby reducing missed orders. The system determines an optimal multiplier to increase compensation based on predictive models of supply and demand and then applying an optimization algorithm to search different hyperparameters that affect how the models generate the multipliers. The system selects the optimal multipliers for different time periods and locations. The system may further present the multipliers being offered during future time periods and enable users to activate reminder alerts for select periods. The offers may be presented in a ranked list using a model trained to infer likelihoods of the user accepting participation and/or setting a reminder notification.