Simulation Gates for Resource Allocation Optimization
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Solution Overview
Problem
Managing resources for future utilization is challenging due to variability and uncertainty, especially when current or near-term resource management affects future availability, making it difficult to optimize resource allocation effectively.
Innovation Solution
A resource management model and a resource utilization model are used to simulate future resource availability, allowing for optimized resource allocation by identifying sub-optimal iterations through the application of simulation gates, which interrupt unnecessary simulations and reduce processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulations are performed to optimize resource allocation, then the accuracy of resource management optimization is improved, but the computational expense and processing time increase
Solution Approach 1:
The patent applies partial action by implementing simulation gates that terminate simulations before they complete when certain conditions are met. The system performs simulations only to the extent necessary to make optimization decisions, stopping early when sub-optimal iterations are identified, thus reducing computational expense while maintaining sufficient accuracy for resource allocation optimization
Solution Approach 2:
The patent implements feedback mechanisms through simulation gates that continuously monitor simulation progress and provide feedback on whether to continue or terminate. This feedback loop allows the system to adjust simulation execution based on real-time performance, preventing wasteful computation on clearly sub-optimal paths while ensuring thorough evaluation of promising resource allocation scenarios
2Manufacturing precision
If multiple simulation iterations are performed to find optimal resource allocation, then the quality of resource allocation is improved, but the processing time increases
Solution Approach 1:
The system performs partial simulations by using simulation gates to stop iterations that are unlikely to produce optimal results. This selective execution approach maintains sufficient simulation quality for good resource allocation decisions while significantly reducing the total number of iterations required, thereby decreasing processing time
Solution Approach 2:
The patent implements skipping by rapidly terminating simulation iterations that fail to meet certain thresholds or show clear signs of sub-optimality. This allows the system to skip through unproductive simulation paths and focus computational resources on promising resource allocation scenarios, reducing overall processing time while maintaining decision quality
3Reliability
If resource management is optimized for future utilization, then the reliability of meeting future needs is improved, but the complexity of resource management increases
Solution Approach 1:
The patent applies preliminary action by using simulations to forecast future resource utilization scenarios before making resource management decisions. The system pre-evaluates multiple possible future states and their implications, allowing resource management to be optimized proactively rather than reactively, which improves reliability while keeping the management process more manageable through structured simulation frameworks
Data Source
AI summary
Aspects described herein utilize a resource management model and a resource utilization model, such that simulations may be performed accordingly. For example, a resource management time period may be a timeframe during which resource management is simulated according to the resource management model, while a resource utilization time period may be the timeframe in which resource utilization is simulated. Optimizing a resource allocation based on such simulations may be computationally expensive, for example due to the number of simulation iterations associated with each optimization iteration. Accordingly, a simulation may utilize one or more gates, which may identify a simulation state that is indicative of a degraded optimization iteration. When such a sub-optimal iteration is identified, the simulation may be interrupted and a subsequent optimization iteration may be simulated instead. Thus, processing time may be reduced for iterations that are unlikely to ultimately result in an optimized resource allocation.