Provisioning Data Pairing for Early Driver-Route Matching
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Solution Overview
Problem
Conventional package delivery systems face challenges in efficiently matching drivers to delivery routes and packages due to uncertainties in package volume and driver availability, leading to suboptimal route assignments and inefficient resource allocation, with a lack of adaptability to changing conditions and insufficient advance notice for drivers.
Innovation Solution
A multi-model machine learning approach that generates driver-facing offers based on predicted package data, creates route plans using actual package data, and pairs these offers with routes, allowing for continuous updating and optimization in real-time to improve driver engagement and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual determination of delivery routes and manual assignment of packages to drivers is used, then flexibility in route planning is maintained, but productivity and efficiency are reduced due to time-consuming manual processes
Solution Approach 1:
The patent replaces manual mechanical processes with automated machine learning models. The first machine learning model generates driver-facing offers based on predicted package data, the second machine learning model creates route plans using actual package data, and the third machine learning model pairs offers with routes. This substitution of manual determination with automated AI-based systems significantly improves productivity while managing system complexity through specialized model architecture.
Solution Approach 2:
The system performs preliminary actions by generating driver-facing offers based on predicted package data before actual package data is fully available. This allows the system to prepare and present delivery opportunities to drivers in advance, improving coordination efficiency by reducing last-minute decisions and enabling better resource allocation before final package details are known.
2Ease of operation
If offers are presented to drivers well in advance of delivery deadlines, then driver availability and satisfaction are improved, but the system must generate and update offers continuously to optimize allocation
Solution Approach 1:
The system implements continuous generation and updating of driver-facing offers, route plans, and pairings throughout the delivery coordination process. The machine learning models continuously refine their outputs based on new package data and changing conditions, ensuring that offers remain optimized and current. This continuous action maintains driver engagement while improving resource allocation efficiency through dynamic adaptation.
Solution Approach 2:
The system incorporates feedback mechanisms where the third machine learning model receives both driver-facing offers and route plans as input to generate optimized pairings. The models continuously learn from actual package data and delivery conditions, adjusting future offer generation to better match driver availability with delivery requirements, thereby improving both driver satisfaction and allocation efficiency.
3Loss of time
If the system generates offers based on predicted package data independent of actual route plans, then offer generation can proceed in advance, but coordination between offers and actual routes becomes more complex
Solution Approach 1:
The patent segments the delivery coordination process into distinct independent components: a first machine learning model for generating driver-facing offers based on predicted package data, a second machine learning model for creating route plans based on actual package data, and a third machine learning model for pairing offers with routes. This segmentation allows each component to operate independently and in parallel, reducing coordination complexity while enabling advance offer generation.
Solution Approach 2:
The third machine learning model acts as an intermediary that receives outputs from both the first model (driver-facing offers) and the second model (route plans) and generates coordinated pairings. This intermediary function bridges the independence of offer generation and route planning, reconciling the two data streams (predicted and actual package data) to produce optimized driver-route assignments without creating complex direct dependencies.
Data Source
AI summary
A system includes a first machine-learning model executed using as input predicted package data to generate a set of provisioning data structures each comprising a predicted region, a predicted duration, and a value, a second machine-learning model executed using as input actual package data to generate a set of routes of provisioning tasks, and a third machine-learning model executed using as input the set of provisioning data structures generated by the first machine-learning model and the set of routes of provisioning tasks generated by the second machine-learning model to generate pairings of provisioning data structures and routes of provisioning tasks.