Nonparametric Framework for Global Production Output Optimization
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Solution Overview
Problem
Existing production systems face challenges in optimizing system output, particularly in determining the optimal combination of control variables to maximize Key Performance Indicators (KPI) values, as traditional methods often get stuck in local optimal solutions and fail to effectively separate variable dependencies.
Innovation Solution
A nonparametric framework using manifold regularization and kernel regression is employed to separate initial input variables into environmental and system response variables, estimating a global input-output mapping function to generate optimal control variables that maximize KPI values, thereby improving production output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optimization methods are used to determine control variables, then the optimization process is simpler, but the system gets stuck in local optimal solutions and fails to achieve global optimality
Solution Approach 1:
The patent segments the optimization process into distinct phases: separating initial input variables into environmental and system response variables, building nonparametric estimation models, and iteratively optimizing control variables. This segmentation allows the complex optimization problem to be broken down into manageable steps while maintaining global optimality through systematic progression.
Solution Approach 2:
The patent performs preliminary separation of variables and builds nonparametric estimation models before actual optimization. By pre-processing the data and establishing the relationship between control variables and system responses in advance, the optimization process can focus on finding global optima without being constrained by local solutions.
2Measurement precision
If traditional variable separation methods are used, then the process is simpler, but the uncertainty in function estimation increases
Solution Approach 1:
The patent extracts and separates environmental variables from system response variables to eliminate sources of uncertainty. By taking out the environmental variables that cannot be controlled, the nonparametric estimation can focus on the relationship between control variables and system responses, reducing estimation uncertainty.
Solution Approach 2:
The patent introduces nonparametric estimation as an intermediary mechanism that bridges the separated variables and the optimization process. This intermediary layer processes the relationship between control variables and system responses without making strong assumptions, reducing estimation uncertainty while managing complexity.
3Productivity
If more control variables are tuned to maximize KPI values, then production output improves, but the complexity of determining optimal combinations increases
Solution Approach 1:
The patent employs dynamic optimization where control variables are adjusted iteratively based on the nonparametric estimation model. Rather than attempting to determine all optimal combinations simultaneously, the system dynamically updates control variables based on observed system responses, making the complexity manageable while maximizing production output.
Solution Approach 2:
The patent changes the parameters of the optimization process by using nonparametric estimation instead of traditional parametric methods. This allows the system to handle multiple control variables more effectively by adapting to the actual relationship between variables and system responses without being constrained by predefined models.
Data Source
AI summary
Systems and methods are provided for optimizing system output in production systems, comprising. The method includes separating, by a processor, one or more initial input variables into a plurality of output variables, the output variables including environmental variables and system response variables. The method also includes building, using the processor, a nonparametric estimation that determines a relationship between one or more initial control variables and the system response variables, and estimating a global input-output mapping function, using the determined relationship, and a range of the environmental variables. The method further includes generating one or more optimal control variables from the initial control variables by maximizing the input-output mapping function and the range of the environmental variables. The method additionally includes incorporating one or more of the optimal control variables into a production system to increase production output of the production system.