Fleet Mission Cycle Control for Bottleneck Queue Reduction
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Solution Overview
Problem
Existing methods for controlling vehicle fleets performing a planned mission cycle in confined or public environments often result in uneven vehicle distribution, leading to queuing at bottleneck areas and decreased productivity.
Innovation Solution
A method that involves mapping a first set of planned degrees of progress to a cycle, controlling vehicles to start at different times, determining deviations from the planned progress, and adjusting to a second set of planned degrees of progress to minimize deviations, allowing for faster progress and increased productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicles are controlled to follow the first set of planned degrees of progress, then the mission cycle is executed according to initial plan, but the vehicles experience unnecessary slowing down and reduced productivity
Solution Approach 1:
The system dynamically adjusts the planned degrees of progress from a static first set to an adaptive second set based on real-time deviation analysis. The control unit continuously monitors actual vehicle progress and recalibrates target progress values, transforming the control system from rigid to flexible and enabling faster overall fleet operation.
Solution Approach 2:
The invention changes the parameter set for planned degrees of progress from the first set to the second set, which is optimized based on actual vehicle performance data. By modifying these progress parameters dynamically, the system eliminates unnecessary slowing down and achieves faster vehicle throughput while maintaining coordinated fleet operation.
2Productivity
If vehicles start the cycle at the same time, then the mission execution is simple to coordinate, but uneven vehicle distribution causes queuing at bottleneck areas
Solution Approach 1:
The system performs preliminary coordination by assigning different start times to vehicles before they enter the mission cycle. This advance scheduling prevents bottleneck queuing by distributing vehicles evenly throughout the cycle, improving productivity while maintaining manageable control complexity through pre-planned timing offsets.
Solution Approach 2:
The invention implements periodic staggering of vehicle start times within the mission cycle. By introducing regular time intervals between vehicle departures, the system achieves uniform vehicle distribution across the fleet operation, preventing bottlenecks and enhancing overall productivity without requiring complex real-time control adjustments.
3Reliability
If vehicles are controlled to minimize deviations from the first set of planned degrees of progress, then the initial mission plan is followed strictly, but the fleet operates slower than potentially achievable
Solution Approach 1:
The system employs feedback mechanisms by continuously determining actual vehicle deviations from planned progress and using this information to adjust the planned degrees of progress. This closed-loop control maintains reliability by ensuring vehicles stay on track while simultaneously improving productivity through data-driven optimization of the second set of progress targets.
Solution Approach 2:
The control system serves itself by automatically recalibrating the planned degrees of progress based on observed fleet performance patterns. Through self-adjustment, the system transitions from rigid adherence to the first set of targets to adaptive followage of the second set, achieving both mission reliability and enhanced productivity without external intervention.
Data Source
AI summary
The invention relates to a method of controlling a plurality of vehicles, performing the same mission cycle, comprising mapping a first set of planned degrees of progress (CCP1) to the cycle, controlling the vehicles to start the cycle at respective different points in time, determining deviations of the vehicles from a respective planned degree of progress (CCP1i) of the first set of planned degrees of progress (CCP1), mapping, based on the determined deviations, a second set of planned degrees of progress (CCP2) to the cycle, and controlling the vehicles so as to minimize deviations of the vehicles from a respective planned degree of progress (CCP2i) of the second set of planned degrees of progress (CCP2).