Robotic Cell Layout Optimization Using Sensitivity Analysis
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Solution Overview
Problem
Existing methods for optimizing robotic cell performance, particularly in cells with multiple robots and workstations, are inefficient and heavily dependent on engineer experience, making it difficult to achieve significant productivity improvements.
Innovation Solution
A method and apparatus that utilize sensitivity analysis to determine factors such as processing time and traveling time affecting robotic cell performance, allowing for targeted performance optimization processes like scheduling and workstation displacement to achieve an improved cell layout.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional try-and-error-loop methods are used for cell layout determination, then the process can be completed with simple tools, but the productivity improvement is insufficient and the process is time-consuming
Solution Approach 1:
The patent performs sensitivity analysis beforehand to identify which workstations and parameters have the greatest impact on cycle time. This preliminary identification allows the optimization process to focus only on critical areas rather than randomly trying different configurations, significantly reducing the time needed to achieve productivity improvements.
Solution Approach 2:
The patent systematically changes parameters such as workstation positions, robot speeds, and processing sequences based on sensitivity analysis results. By identifying which parameters have the highest sensitivity to cycle time changes, the system can make targeted parameter adjustments that yield maximum productivity improvement with minimum optimization time.
2Productivity
If sensitivity analysis is performed to identify critical workstations and parameters, then the optimization becomes targeted and efficient, but the complexity of the optimization process increases
Solution Approach 1:
The patent divides the optimization process into distinct segments: sensitivity analysis phase, critical parameter identification phase, and targeted optimization phase. This segmentation allows each phase to be handled with appropriate complexity - the sensitivity analysis uses systematic variation methods, while the optimization phase focuses only on identified critical parameters, preventing overwhelming complexity.
Solution Approach 2:
The patent introduces a structured parameter change approach where sensitivity analysis first identifies which parameters matter most, then subsequent optimization only adjusts those specific parameters. This selective parameter management reduces the effective complexity of the optimization process compared to attempting to optimize all parameters simultaneously.
3Reliability
If workstation placement and operation sequences are repeatedly rearranged through empiristic trials, then performance requirements can be met, but the process is severely dependent on engineer experience and lacks efficiency
Solution Approach 1:
The patent implements feedback through sensitivity analysis that quantifies how changes in workstation placement and operation sequences affect cycle time. This feedback mechanism replaces subjective engineer judgment with objective data, showing exactly which changes will improve performance and by how much, making the optimization process less dependent on individual experience.
Solution Approach 2:
The system performs self-optimization by automatically identifying critical parameters and determining optimal adjustments through sensitivity analysis, reducing the need for engineer intervention and empirical trial-and-error. The methodology enables the optimization process to guide itself based on calculated sensitivities rather than requiring continuous human judgment.
Data Source
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AI summary
Embodiments of the present disclosure relate to a method and apparatus for optimizing performance of a robotic cell. The robotic cell comprises at least one workstation and at least one robot. The method may comprise determining a factor and a corresponding workstation sensitive to the performance of the robotic cell by performing a sensitivity analysis on an initial cell layout for the robotic cell; and performing a performance optimization process on the corresponding workstation based on the determined factor to obtain an improved cell layout for the robotic cell. With embodiments of the present disclosure, it is possible to perform performance optimization of the robotic cell on the sensitive workstation regarding to the sensitive factor, and thus it may efficiently determine an improved cell layout which may achieve performance improvement.