Decision Computing System for Multi-Dimensional Resource Allocation
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Solution Overview
Problem
Current decision computing systems are inadequate for automating real-time resource allocation in complex environments, such as coordinating sensors, weapons, and first responder resources, as they are not designed to handle multi-dimensional problems effectively.
Innovation Solution
A method that uses the Analytic Hierarchy Process (AHP) to prioritize tasks, combine solutions, determine candidate scores, and select optimal responses based on weighted scoring, enabling real-time planning and re-planning of asset tasking across multiple networks and sub-networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional decision computing systems are used, then single task resolution is achieved, but multi-dimensional resource allocation problems cannot be handled
Solution Approach 1:
The system segments the complex multi-dimensional resource allocation problem into hierarchical levels: scenario-level objectives, task-level requirements, and resource-level assignments. This segmentation allows the system to handle complexity by breaking it down into manageable decision layers that can be processed independently and then integrated.
Solution Approach 2:
The patent introduces a temporal dimension by implementing real-time processing capabilities that continuously evaluate and reassign resources as new information becomes available. This transforms the static single-task decision system into a dynamic multi-dimensional system that operates across time, resource types, and task priorities simultaneously.
2Productivity
If real-time resource allocation is implemented, then decision-making efficiency is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing scenario templates with predefined objectives, constraints, and resource requirements. These templates are prepared in advance and can be quickly instantiated when scenarios arise, eliminating the need to build decision models from scratch in real-time and significantly reducing computational burden during actual resource allocation.
Solution Approach 2:
The patent implements dynamic resource allocation where the system continuously adapts resource assignments based on changing conditions. The computational model dynamically adjusts task priorities, resource availability, and scenario parameters in real-time, allowing the system to maintain decision-making efficiency while handling complexity through adaptive rather than static processing.
3Reliability
If multiple task types are coordinated, then resource optimization is improved, but computational time increases
Solution Approach 1:
The system merges multiple task type coordinations into a unified decision framework that processes all task types simultaneously rather than sequentially. By combining sensor coordination, weapon assignment, and resource allocation into a single integrated optimization process, the system achieves comprehensive resource optimization across all task types without the time penalty of multiple separate computational passes.
Data Source
AI summary
A representative method for coordinating and optimizing resources in the completion of a set of tasks includes providing multiple task types defined for a scenario and task priorities; combining multiple solutions of the respective multiple task types into multiple candidate decisions; determining candidate scores for the respective multiple candidate decisions based on the combined multiple solutions of the respective multiple task types; determining an optimal candidate score based on the candidate scores from the respective multiple candidate decisions and task priorities; and selecting an optimal response to a given scenario based on the candidate decision based on having the determined optimal candidate score.