Driver Scheduling With Rolling Horizon Optimization Updates
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Solution Overview
Problem
Existing driver scheduling systems are cumbersome and slow, often resulting in stale schedules due to long computation times, which can become obsolete before completion, especially when using less powerful processors or mobile devices.
Innovation Solution
A system and method utilizing linear programming optimization logic, such as rolling horizon optimization with column generation, to quickly generate and update driver schedules on devices with reduced processing power, allowing for real-time or near-real-time scheduling adjustments based on dynamic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional scheduling algorithms are used to optimize driver schedules, then scheduling completeness can be achieved, but scheduling time becomes excessively long (hours or days)
Solution Approach 1:
The patent segments the scheduling problem into multiple smaller sub-problems that can be solved independently and in parallel. By dividing the overall optimization task into manageable chunks, the system achieves both computational completeness and reduced scheduling time, resolving the contradiction between thorough scheduling and time efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-processing scheduling data and preparing optimization parameters before the actual scheduling optimization. This preliminary preparation enables faster execution of the complete scheduling algorithm, thereby reducing overall scheduling time while maintaining scheduling completeness.
2Adaptability or versatility
If optimization is performed on mobile devices with less powerful processors, then scheduling portability is improved, but computation speed decreases
Solution Approach 1:
The patent changes key parameters of the optimization algorithm to reduce computational complexity and resource requirements. By adjusting parameters such as iteration limits, precision levels, and problem segmentation granularity, the system enables efficient scheduling optimization on mobile devices with limited processing power while maintaining acceptable scheduling quality.
3Manufacturing precision
If schedules are optimized in advance, then scheduling thoroughness is improved, but schedule freshness deteriorates (schedules become obsolete)
Solution Approach 1:
The system implements continuous scheduling optimization by repeatedly executing the optimization process as new data becomes available. This continuous action ensures that schedules remain fresh and up-to-date while maintaining thorough optimization, resolving the contradiction between scheduling completeness and schedule freshness.
Data Source
AI summary
A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: generating a schedule summary comprising one or more selectable portions of availability for drivers; receiving a selection of an unfilled portion of a respective schedule for a respective driver of the drivers adds the respective driver to a list of respective drivers to be scheduled on the schedule summary displayed on a GUI of an electronic device of a user; receiving one or more selections of the one or more selectable portions of availability of the schedule summary causes one or more changes to occur on the schedule summary displayed on the GUI of the electronic device of the user; and displaying one or more schedules for the drivers. Other embodiments are disclosed herein.