Ridesharing Shift Planning for Driver-Request Mismatch
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Solution Overview
Problem
Current ridesharing management systems face inefficiencies due to mismatches between the number of ride requests and available drivers, leading to excess or shortage of drivers and suboptimal scheduling.
Innovation Solution
A system and method for managing a fleet of ridesharing vehicles that determines a plan for driver shifts based on ride requests, driver limitations, and vehicle availability, segmenting shifts for optimal assignment of requests, and iteratively refining the plan to accommodate all requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ridesharing management systems use prescheduled drivers and fixed scheduling, then driver availability is ensured, but scheduling inefficiencies occur and driver mismatch (excess or shortage) happens
Solution Approach 1:
The system transitions from static prescheduled driver assignments to dynamic shift plan generation that adapts to real-time ride request patterns. The processor dynamically creates optimized shift plans based on current demand, driver availability, and ride request characteristics, allowing the scheduling system to flexibly respond to changing conditions rather than relying on fixed preschedules
Solution Approach 2:
The system changes key scheduling parameters including shift duration, start times, and driver assignments based on ride request analysis. By dynamically adjusting these parameters rather than using fixed values, the system optimizes both driver utilization and ride request fulfillment efficiency
2Ease of operation
If the system assigns drivers based on simple prescheduling, then implementation is straightforward, but driver mismatch (excess or shortage) occurs
Solution Approach 1:
The system performs preliminary analysis of ride request patterns and driver availability before creating shift plans. By pre-processing ride request data and driver information to identify optimal assignments, the system ensures proper driver quantity matching while maintaining operational simplicity through automated decision-making
Solution Approach 2:
The system creates optimized shift plan copies that can be replicated and assigned to multiple drivers based on identified patterns. This allows straightforward implementation through template-based assignment while ensuring proper driver quantity matching through systematic replication of successful shift configurations
3Reliability
If the system creates detailed shift plans for all ride requests, then complete coverage is achieved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the ride request portfolio into groups based on characteristics such as time, location, and demand patterns. By creating shift plans for segments rather than attempting to optimize all requests simultaneously, the system achieves complete coverage while reducing computational complexity through divide-and-conquer processing
Solution Approach 2:
The system generates shift plans that may cover more ride requests than strictly necessary, allowing some overlap and redundancy. This ensures complete coverage of all ride requests while simplifying the planning process by not requiring perfect optimization of every single assignment
Data Source
AI summary
Systems and methods for determining plans for ridesharing vehicle shifts for a plurality of ride requests that can provide an optimal plan are provided. The systems and methods can involve iteratively planning shifts such that a maximal number of rides can be schedules within a shift within one or more limitations.