This study showcases the use of Multi-Sigma® to conduct multi-objective optimization aimed at identifying the optimal trade-off between two conflicting automotive performance criteria: fuel efficiency and engine power.
1. Prediction of fuel efficiency and engine power performance
The prediction function of Multi-Sigma® enables the construction of an AI model that captures the relationship between input data and output data. Using this AI model, it is possible to predict two performance indicators—fuel efficiency and engine power—based on six parameters: engine displacement, vehicle weight, acceleration performance, model year, number of cylinders, and country of origin.
2. Contribution Analysis
The contribution analysis function of Multi-Sigma® enables the identification of factors that contribute positively (and negatively) to performance indicators. It was quantitatively confirmed that while vehicle weight has a significant impact on both performance metrics, the directions of its influence are opposite.
3. Multi-objective optimization of performance indicators
The optimization function of Multi-Sigma® can propose optimal combinations of input parameter values to achieve the desired performance indicators.
Note: The data used in this analysis is processed and edited based on the data published in the article below, under Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Data Source: https://archive.ics.uci.edu/dataset/9/auto+mpg