Time-step Optimization Constraint Adaptation
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Solution Overview
Problem
Existing optimization methods for long-term target achievement are inefficient, particularly when dealing with large optimization regions and uncertain variables, leading to outdated results and high computational burdens, making them unsuitable for real-time decision-making.
Innovation Solution
The approach converts a multiple time-step optimization problem into a sequence of single time-step optimization problems, with target values adjusted over time to ensure efficient long-term target achievement by considering cumulative target divergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optimization is performed over a large time period to achieve long-term targets, then the long-term target achievement is improved, but the computation time and complexity increase significantly
Solution Approach 1:
The patent divides the long-term optimization problem into multiple shorter time-period segments. Each segment is optimized independently with adjusted target values, allowing the system to achieve long-term goals without computing the entire long-term optimization at once. This segmentation reduces computation time while maintaining target achievement reliability.
Solution Approach 2:
The patent dynamically adjusts target values for each time period based on cumulative progress toward the long-term target. As the optimization progresses through different time periods, the target values are updated to reflect remaining goals, enabling adaptive optimization that maintains effectiveness without requiring re-optimization of the entire long-term horizon.
2Duration of action of stationary object
If the optimization region dimension is increased to cover more time periods, then the long-term target coverage is improved, but the optimization complexity and resource usage increase
Solution Approach 1:
The patent segments the optimization region into multiple smaller time-period regions rather than treating it as one large region. Each segment has its own optimized target values and constraints, reducing the complexity of each individual optimization problem while collectively covering the entire long-term horizon.
Solution Approach 2:
The patent performs optimization for specific time periods with adjusted target values rather than optimizing the entire long-term region simultaneously. This partial action approach allows the system to handle complexity by focusing on manageable time segments while still achieving comprehensive long-term goals.
3Speed
If real-time decisions are required based on optimization results, then the decision-making speed is improved, but the optimization must be faster and more efficient
Solution Approach 1:
By segmenting the optimization into shorter time periods, the patent reduces the computation time required for each optimization run. This allows the system to generate optimization results quickly enough for real-time decision-making while still addressing long-term goals through sequential optimization of the segmented periods.
Solution Approach 2:
The dynamic adjustment of target values allows the optimization to adapt to changing conditions and prioritize immediate decisions over long-term planning in each time period. This enables the system to produce timely optimization results that support real-time decision-making while maintaining alignment with long-term objectives.
Data Source
AI summary
Methods and systems of optimization constraint adaptation for long-term target achievement. One system includes an electronic processor configured to divide a multiple time-step optimization problem into a plurality of successive single time-step optimization problems. The processor is configured to determine a first optimal variable value for a first single time-step optimization problem and determine a first resulting value of a secondary quantity based on the first optimal variable value. The processor is configured to determine a first divergence of the first resulting value from a first target value and determine a cumulative target divergence based on the first divergence. The processor is configured to determine a first target value adjustment for a second time-step based on the cumulative target divergence, adjust a first original target value of the secondary quantity for the second time-step using the first target value adjustment, and output the adjusted first original target value for display.