Stochastic Parameter Generation for Multi-Constraint Item Production
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for producing new items are inefficient and dependent on skilled personnel, random selection of factors, or inaccurate mathematical optimization, failing to discover multiple methods for achieving desired results.
Innovation Solution
A parameter generation device and method that applies stochastic fluctuations to objective functions and constraint conditions to generate multiple parameter sets for producing desired items, using predictive models and optimization processing to derive optimal combinations of factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple combinations of factors are derived for new item exploration, then the ability to discover multiple production methods is improved, but the complexity of deriving and evaluating combinations increases
Solution Approach 1:
The patent segments the complex problem of finding optimal factor combinations into two distinct phases: (1) generating diverse candidate combinations using stochastic fluctuation methods, and (2) evaluating these combinations using predictive models. This segmentation allows the system to handle multiple combinations systematically without being overwhelmed by complexity.
Solution Approach 2:
The patent introduces an intermediary predictive model that bridges the gap between generating factor combinations and evaluating their effectiveness. This intermediary component enables efficient assessment of multiple combinations without requiring exhaustive experimentation, thus managing complexity while maintaining versatility.
2Measurement precision
If mathematical programming solver is used to derive optimal solution, then optimization accuracy is improved, but the efficiency of engineers decreases due to need to design optimization problem each time
Solution Approach 1:
The patent enables the system to serve itself by automatically generating objective functions through stochastic fluctuation of reference values. This self-service capability eliminates the need for engineers to manually design optimization problems each time, thereby maintaining high optimization accuracy while significantly improving engineering efficiency.
Solution Approach 2:
The patent performs preliminary generation of diverse candidate combinations using stochastic methods before applying mathematical optimization. This preliminary action prepares the optimization problem in advance, reducing the manual effort required while maintaining accuracy in the final optimization step.
3Ease of operation
If random selection of factor combinations is used, then the independence from skilled personnel is improved, but the efficiency decreases as conditions become more complex
Solution Approach 1:
The patent transforms the random selection approach by systematically changing parameters through stochastic fluctuation. Instead of purely random selection, the system generates combinations by fluctuating reference values according to defined probability distributions, maintaining independence from skilled personnel while dramatically improving efficiency even for complex conditions.
4Reliability
If single objective function optimization is performed, then the convergence to optimal solution is improved, but the ability to discover multiple production methods is reduced
Solution Approach 1:
The patent introduces dynamics into the optimization process by generating multiple objective functions through stochastic fluctuation. Instead of a static single objective function, the system dynamically creates varied objective functions that explore different regions of the solution space, enabling convergence to multiple valid production methods while maintaining reliability.
Data Source
AI summary
The input means 81 accepts input of a first objective function and a constraint condition defining a combination of factors related to item production. The objective function generation means 82 generates a second objective function by applying stochastic fluctuation to a parameter of the first objective function. The optimization processing means 83 performs optimization of a model including the second objective function and the constraint condition. The output means 84 outputs a value of a variable of the second objective function obtained by the optimization as a parameter set.