Iterative Operational Constraint Specification for System Optimization
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Solution Overview
Problem
Configurable systems, such as manufacturing plants, face challenges in setting optimal operational parameters due to dynamic business and operational objectives, which are difficult to translate into static values for configurable operating parameters, leading to imprecision in decision-making and conflicting objectives.
Innovation Solution
A method and apparatus that present user-selectable decision questions to elicit operational goals, using an optimization engine to adjust configurable parameters based on mathematical models, allowing users to iteratively specify and re-specify goal indications and tolerances, thereby controlling the determination of operational parameters to achieve desired objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static values are used for configurable operating parameters, then system configuration is simplified, but imprecision in decision-making occurs due to inability to capture dynamic business and operational objectives
Solution Approach 1:
The system transforms static parameter configuration into a dynamic process by implementing iterative specification of operational constraints. Users can repeatedly adjust and refine constraint values across multiple iterations, allowing the system to adapt to changing business and operational objectives while maintaining precise decision-making capability
Solution Approach 2:
The system performs preliminary optimization by pre-calculating configurable parameter values based on initially specified constraints before user review. This allows users to start with pre-computed recommendations and then iteratively refine them, reducing the initial complexity burden while maintaining precision
2Productivity
If multiple operational objectives are simultaneously optimized, then system performance improves, but conflicting objectives create decision-making difficulties
Solution Approach 1:
The system segments multiple conflicting operational objectives into separate, independently specifiable constraints. Each objective can be defined and adjusted individually through the iterative specification process, allowing users to manage and resolve conflicts between objectives systematically rather than dealing with them as an undifferentiated complex problem
Solution Approach 2:
The system enables independent adjustment of parameter values for each operational objective through iterative constraint specification. Users can modify constraint values for individual objectives across iterations, allowing flexible trade-off analysis and conflict resolution between multiple objectives without requiring simultaneous optimization of all parameters
3Measurement precision
If business and operational objectives are directly translated to configurable parameters, then decision-making precision improves, but the complexity of determining proper parameter values increases
Solution Approach 1:
The system introduces an intermediary optimization layer that automatically translates business and operational objectives into configurable parameter values. This intermediary process handles the complex translation work, presenting users with refined parameter recommendations that maintain precision while reducing the direct complexity of manual parameter determination
Solution Approach 2:
The system implements feedback through iterative specification where users can review determined parameter values and constraint specifications, then adjust them in subsequent iterations. This feedback loop allows users to control the determination process progressively, maintaining precision while managing complexity through incremental refinement rather than requiring complete parameter determination upfront
Data Source
AI summary
A system and method for determining parameters for a system. Selectable questions with associated goal are presented. Operational goals are different from configurable parameters of the system. A question is selected and a goal indication is received. First values for each goal are determined by an optimization engine adjusting parameters of mathematical models for the system to improve a value of the goal associated with the selected question in a direction of the goal indication. Selectable questions and the first values are presented and selection of a second question and a second goal indication is received. An optimization engine determines second updated values by adjusting parameters of the mathematical model to improve a value of a goal associated with the second selected question in the direction of the second goal indication. Second updated values of the goals, and differences from the first updated values. are presented.