Combinatorial solution determination system
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Solution Overview
Problem
Mathematical programming problems without a formulated objective function, such as black-box optimization problems, require excessive computation time to derive optimal solutions, particularly in selecting energy-efficient air conditioning systems where device performance and varying heat loads complicate the selection process.
Innovation Solution
A combinatorial solution determination system comprising a candidate solution generation unit, simulation unit, evaluation index calculation unit, and necessary-series-data extraction unit, which reduces computational cost by generating candidate solutions, calculating simulation data, evaluating indices, and extracting necessary series data using sparse estimation and extreme value statistics to efficiently derive optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all series data is used for simulation calculations, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The necessary-series-data extraction unit extracts only the essential series data required for evaluation index calculation from the complete set of simulation data. This extraction process identifies and isolates the critical data points that contribute most significantly to accurate evaluation, eliminating redundant data and reducing computation time while preserving measurement precision.
Solution Approach 2:
The system performs partial action by calculating simulation data for only a subset of candidate combinatorial solutions (m solutions) rather than all n solutions. The necessary-series-data extraction unit then extracts essential data from this partial set, which is sufficient for determining the optimal solution without requiring complete data processing for all candidates.
2Reliability
If simulation data is calculated for all candidate combinatorial solutions, then reliability is improved, but productivity decreases
Solution Approach 1:
The candidate solution generation unit generates multiple candidate combinatorial solutions in advance before the simulation phase. The necessary-series-data extraction unit then preliminarily identifies the essential data required for evaluation from these pre-generated candidates, allowing the simulation unit to focus calculations only on the most promising candidates rather than processing all possibilities.
Solution Approach 2:
The necessary-series-data extraction unit extracts only the critical series data needed for reliable solution determination from the complete simulation dataset. This extraction enables the system to maintain high reliability by preserving the essential information while eliminating redundant data that would slow down the productivity of solution derivation.
3Manufacturing precision
If complete series data is processed, then manufacturing precision is improved, but loss of time worsens
Solution Approach 1:
The necessary-series-data extraction unit extracts only the essential series data required for accurate evaluation index calculation from the complete simulation data. This extraction process maintains manufacturing precision by preserving the critical data points needed for accurate solution determination while eliminating redundant data that would increase processing time.
Solution Approach 2:
The data processing is segmented into distinct phases: the candidate solution generation unit creates candidate solutions, the necessary-series-data extraction unit extracts essential data from simulation results, and the solution determination unit processes only this extracted data. This segmentation allows each unit to operate efficiently on optimized data subsets, improving overall processing speed while maintaining accuracy.
Data Source
AI summary
A combinatorial solution determination system includes a candidate solution generation unit that generates candidate combinatorial solutions, a simulation unit, an evaluation index calculation unit, a solution determination unit, and a necessary-series-data extraction unit. The simulation unit calculates simulation data using information related to the candidate combinatorial solutions, and series data to evaluate a combinatorial solution. The evaluation index calculation unit calculates evaluation indices based on the simulation data. The solution determination unit determines a combinatorial solution having high evaluation from the candidate combinatorial solutions based on the evaluation indices each calculated by the evaluation index calculation unit from a corresponding one of the candidate combinatorial solutions. The necessary-series-data extraction unit extracts second and third series data from the first series data, and combines the second and third series data and acquire the second and third series data as necessary series data.