Dynamic Offer Publish Time for Delivery Lag Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems face challenges in optimizing the publish time for delivery orders, leading to driver lag time and inefficiencies in delivery processes, particularly in managing millions of products across marketplaces, where static offer publish criteria result in wasted resources and bottlenecks.
Innovation Solution
A system utilizing machine learning models and optimization techniques to analyze historical driver search information, determining dynamic offer publish times that reduce driver lag time by processing orders through a driver search platform, thereby improving delivery efficiency and maintaining on-time arrivals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static offer publish time criteria are used, then system simplicity is maintained, but driver lag time increases and delivery efficiency decreases
Solution Approach 1:
The patent applies dynamics by transitioning from static offer publish time criteria to dynamic criteria that adapt based on real-time driver search behavior and historical data. The system continuously adjusts publish times based on learned patterns, making the system responsive to changing conditions rather than relying on fixed schedules.
Solution Approach 2:
The patent changes the parameter of offer publish time from a fixed value to a dynamically adjusted value based on driver search information. By modifying this critical parameter based on historical data and machine learning insights, the system optimizes delivery efficiency while reducing driver lag time.
2Productivity
If machine learning models are implemented to optimize publish times, then delivery efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as an intermediary between raw driver search data and offer publish time decisions. This intermediary processes complex data patterns and translates them into optimized publish time criteria, managing system complexity while improving delivery efficiency.
Solution Approach 2:
The system implements feedback loops where driver search information and historical delivery data are continuously fed into machine learning models, which then adjust offer publish time criteria. This feedback mechanism enables continuous optimization while managing complexity through iterative improvement rather than complex upfront design.
3Loss of time
If dynamic offer publish times are implemented, then driver lag time is reduced, but measurement and analysis complexity increases
Solution Approach 1:
The patent creates simplified representations (copies) of complex driver search behavior patterns through machine learning models. Instead of directly analyzing raw, complex driver search data, the system uses trained models that capture essential patterns, making measurement and analysis more manageable while still reducing driver lag time effectively.
Data Source
AI summary
Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving historical driver search information corresponding to a first offer publish time criterion, the first offer publish criterion including a driver lag time; building a machine learning model based on the driver search information to determine a first metric and a second metric; analyzing the first metric and the second metric with an optimization model to determine a second offer publish time criterion that reduces the driver lag time; receiving an order for a delivery for an item, the order including a delivery time window; transmitting the order to a driver search platform subject to the second offer publish time criterion to reduce the driver lag time and mitigate delivery outside of the delivery time window. Other embodiments are disclosed herein.