Fleet Routing Exception Control With AI Schedule Recovery
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Solution Overview
Problem
Fleet logistics face challenges in ensuring timely ride services due to delays caused by vehicle breakdowns, driver lateness, or traffic, leading to cascading impacts on route schedules.
Innovation Solution
A vehicle routing system utilizing a server computer with AI analysis to detect exceptions to route schedules, identify resolutions based on historical data, and automatically implement corrections, including vehicle reassignment and schedule adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring and control of fleet vehicles is used, then system complexity is reduced, but route schedule accuracy and responsiveness to delays deteriorate
Solution Approach 1:
The system enables self-service through automated exception detection and resolution. The server computer automatically monitors vehicle locations, detects schedule exceptions, analyzes historical data, and implements resolutions without manual intervention. This automation maintains high route schedule accuracy while the system manages its own operations independently.
Solution Approach 2:
The system implements continuous feedback loops by monitoring vehicle locations in real-time, comparing actual progress against scheduled routes, detecting exceptions when deviations occur, and automatically adjusting schedules based on historical data analysis. This closed-loop feedback ensures high schedule accuracy through automated corrections.
2Productivity
If automated exception detection and resolution is implemented, then route schedule accuracy improves, but system complexity increases
Solution Approach 1:
The system replaces manual mechanical monitoring and decision-making processes with automated electronic systems. The server computer uses AI algorithms to analyze historical data and real-time vehicle locations, automatically detecting exceptions and implementing resolutions without human intervention, thereby improving productivity despite increased electronic system complexity.
Solution Approach 2:
The system performs preliminary actions by pre-analyzing historical data to predict potential exceptions and preparing resolution strategies before exceptions occur. When exceptions are detected, pre-planned resolutions are automatically implemented, improving fleet operation efficiency through proactive management.
3Reliability
If real-time monitoring of all vehicles is performed, then exception detection accuracy improves, but energy consumption increases
Solution Approach 1:
The system achieves universal monitoring where a single server computer performs multiple functions: tracking vehicle locations, detecting exceptions, analyzing historical data, and implementing resolutions. This multi-functional approach maintains high exception detection accuracy while consolidating energy consumption into a centralized system rather than requiring energy-intensive monitoring at each vehicle.
Data Source
AI summary
A system and method include a server computer that determines a plurality of routes and corresponding route schedules for a plurality of ride service requests. The server computer assigns a plurality of vehicles to service each one of the plurality of routes and further assigns one of the plurality of vehicles to one of a plurality of drivers to perform the route according to the route schedule. The server computer may detect an exception to the route schedule, identify a resolution to the exception, and automatically implement the resolution to the exception.