Simulation Collaborator Selection Using Aptitude and Line-Planning History
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Solution Overview
Problem
Existing systems face challenges in selecting an appropriate production collaborator for simulation tasks, especially when the scale of the simulation is large, due to an increased number of candidates and complexity in worker selection.
Innovation Solution
A simulation system and computer-readable medium that calculates an aptitude level for workers based on historical data of simulation cooperation and line design planning to identify a leading candidate for simulation tasks, facilitating accurate selection of production collaborators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the scale of the simulation is increased, then the comprehensiveness of the simulation improves, but the complexity of selecting production collaborators increases
Solution Approach 1:
The system segments the large-scale simulation into multiple simulation tasks, each associated with specific real tasks. For each simulation task, the system independently calculates aptitude levels and selects production collaborators, breaking down the complex selection process into manageable units. This allows the system to handle large-scale simulations while maintaining manageable collaborator selection complexity through task-level granularity.
2Ease of operation
If manual selection of production collaborators is performed, then flexibility in selection is maintained, but the time and effort required increases
Solution Approach 1:
The system performs preliminary calculations of aptitude levels for all practical workers based on historical data before the actual simulation task assignment. This pre-computation stores worker capabilities in advance, so when simulation tasks need to be assigned, the system can quickly retrieve and match workers without performing time-consuming manual evaluations, thus reducing selection time while maintaining flexibility.
Solution Approach 2:
The system uses historical data of simulation cooperation and line design planning as feedback to continuously improve aptitude level calculations. This feedback mechanism learns from past performance, making the selection process increasingly accurate and efficient over time, reducing the time required for future selections while maintaining high flexibility in choosing appropriate collaborators.
3Measurement precision
If aptitude level calculation is performed for each simulation task, then selection accuracy improves, but the computational processing time increases
Solution Approach 1:
The system performs preliminary calculation of aptitude levels for all practical workers based on their historical data before simulation tasks are assigned. These pre-calculated aptitude levels are stored and can be quickly retrieved during task assignment, eliminating the need to recalculate from scratch for each task. This maintains high selection accuracy while significantly reducing processing time during actual simulation setup.
Data Source
AI summary
Processing to select a production collaborator of a simulation is performed. In the processing to select a production collaborator, an aptitude level of a practical worker for a simulation task set in association with a real task constituting an operation performed in a line is calculated based on historical data of a simulation cooperation by the practical worker and historical data of a line design planning by the practical worker. In addition, a leading candidate of the production collaborator is specified based on the calculated aptitude level. Then, information on the identified leading candidate is transmitted to a computer of a producer of the simulation.