Integrated ML Prediction and Optimization for Decision-Making
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Solution Overview
Problem
Current machine learning systems are separate from and incompatible with optimization systems, leading to inefficient and error-prone processes for embedding ML models into optimization models for decision-making, lacking automated methods for ML model selection and complexity evaluation, and feedback on optimization solution quality.
Innovation Solution
An integrated optimized machine learning system that combines machine learning and optimization through automated end-to-end prediction-optimization, using ML model selection algorithms and feedback loops to generate control inputs and predicted outputs, leveraging models like deep neural networks and decision trees, and incorporating user preferences and constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are combined with optimization systems, then decision-making quality is improved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent merges machine learning prediction functionality with optimization decision-making in a unified system architecture. The ML models are integrated as predictive components within the optimization framework, allowing seamless interaction between prediction and optimization processes to improve overall decision-making quality while managing integration complexity through unified system design.
Solution Approach 2:
The system employs universal interfaces and standardized data exchange protocols that allow the ML models to serve multiple functions - both prediction and optimization inputs. This multi-functionality reduces integration complexity by using common communication standards across different system components.
2Productivity
If automated ML model selection is implemented, then model selection efficiency is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary evaluation of ML models during the training phase, pre-assessing model performance metrics and compatibility with optimization objectives. This preliminary action allows for faster model selection during deployment, as the computational heavy lifting is done in advance during training rather than at selection time.
Solution Approach 2:
The patent replaces manual model selection processes with automated algorithms that use computational metrics and performance indicators to objectively evaluate and select models. This substitution of automated computational methods for manual evaluation improves efficiency while managing processing time through algorithmic optimization.
3Measurement precision
If feedback loops are added for solution quality evaluation, then optimization accuracy is improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent implements feedback loops where optimization results are evaluated against actual outcomes, and this feedback is used to refine both the optimization parameters and ML model predictions. The feedback mechanism uses standardized metrics and automated evaluation protocols to maintain accuracy while controlling complexity through systematic feedback processing.
4Measurement precision
If multiple ML models are trained and evaluated, then prediction accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The system trains multiple ML models with varying degrees of complexity and evaluates them to select the most appropriate model for each specific optimization task. Rather than always using the most complex model, the system applies partial action by selecting only the necessary model complexity required for each prediction task, thus improving accuracy where needed while reducing unnecessary computational overhead.
Data Source
AI summary
A method includes training, by one or more processing devices, a plurality of machine learning predictive models, thereby generating a plurality of trained machine learning predictive models. The method further includes generating, by the one or more processing devices, a solved machine learning optimization model, based at least in part on the plurality of trained machine learning predictive models. The method further includes outputting, by the one or more processing devices, one or more control input and predicted outputs based at least in part on the solved machine learning optimization model.