Stakeholder Feedback Loops in System Design Optimization
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Solution Overview
Problem
Conventional system design optimization methods lack direct integration of human expertise, fail to adapt to unexpected changes, converge on local optima, and are inefficient for complex systems, leading to longer development periods.
Innovation Solution
A system design optimization system that incorporates feedback from stakeholders, particularly system engineers, using reinforcement learning to iteratively improve the design by transforming their insights into actionable changes, ensuring alignment with user needs and system performance goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional computational models are used for system design optimization, then automation is achieved, but human expertise and nuanced insights are not integrated
Solution Approach 1:
The system merges automated computational models with human expert feedback by integrating a feedback collection module that continuously gathers insights from domain experts and systematically incorporates them into the optimization process, combining the strengths of both automated processing and human judgment
Solution Approach 2:
The system implements continuous feedback loops where expert opinions are collected, processed, and fed back into the optimization algorithm to guide the search process, ensuring that human expertise directly influences design decisions while maintaining automation
2Extent of automation
If iterative search and optimization processes are performed relying on computational sources, then automation is enhanced, but efficiency decreases for complicated target systems
Solution Approach 1:
The system introduces domain experts as intermediaries between the computational model and the design space, where experts provide guidance on complex system behaviors and constraints that pure computational approaches struggle to capture, thereby improving optimization efficiency for complicated systems
Solution Approach 2:
The system performs preliminary actions by having experts provide initial feedback and constraints before the full iterative optimization process begins, allowing the computational model to start from a more informed state and reducing the overall optimization time for complex systems
3Extent of automation
If algorithmic approaches are used for optimization, then automation is achieved, but the system may converge on local optima and miss fundamental design changes
Solution Approach 1:
The system uses expert feedback as a guiding mechanism to prevent convergence on local optima, where domain experts identify when the optimization is stagnating and suggest directional changes that lead to fundamental design improvements and global optima
Solution Approach 2:
The system dynamically adjusts the optimization process by incorporating real-time expert feedback that can fundamentally alter the search direction, allowing the system to transition from static algorithmic search to a dynamic process that adapts to avoid local optima and discover better solutions
4Device complexity
If conventional optimization systems are used, then initial model processing is performed, but they cannot adapt to unexpected design requirements or changes
Solution Approach 1:
The system implements continuous feedback collection from experts throughout the optimization process, enabling real-time detection and adaptation to unexpected design requirements or changes that were not anticipated in the initial model
Solution Approach 2:
The system transforms the static initial model into a dynamic optimization process that continuously adapts to new requirements through expert feedback, allowing the system to evolve and respond to unexpected changes rather than being constrained by the original model assumptions
5Productivity
If stakeholder feedback is not incorporated into the optimization process, then computational efficiency is maintained, but the design may not fulfill real needs
Solution Approach 1:
The system integrates stakeholder feedback collection and processing directly into the optimization workflow, where feedback is systematically incorporated to guide the search toward solutions that truly fulfill real needs while maintaining computational efficiency through structured feedback processing
Solution Approach 2:
The system creates a multi-functional optimization framework that simultaneously performs computational optimization and stakeholder needs assessment, combining these previously separate functions into a unified process that ensures both efficiency and alignment with real requirements
Data Source
AI summary
A system design optimization system that optimizes design of a target system is configured to acquire feedback related to an improvement of the target system from a stakeholder of the target system, interpret the acquired feedback to apply the feedback to the target system, generate, from a result of the interpretation, an instruction set to be given to the target system, provide the stakeholder with an implementation status of the instruction set, and acquire the feedback related to the improvement of the target system until the stakeholder approves.