Decision Trees and Multi-objective Optimization to Calibrate Process Parameters
Series/Report No.
Lecture Notes in Mechanical Engineering
Part Of
International Conference on Flexible Automation and Intelligent Manufacturing
Start Page
476
End Page
483
ISSN
2195-4356
2195-4364
ISBN
9783031744846
9783031744853
Date Issued
2024
Author(s)
DOI
10.1007/978-3-031-74485-3_52
Abstract
The paper introduces an innovative approach to address the challenge of determining process parameter settings to achieve desired goals. We propose a mathematical programming approach that integrates a multi-objective formulation with decision tree models. These decision tree models predict different targets, and the mathematical model combines their outputs with other process requirements. A case study demonstrates the effectiveness of our model and compares its performance to a method based on a multi-objective decision tree. The results reveal that our method, employing multiple single-objective decision trees, outperforms the conventional approach utilizing a single multi-objective decision tree. The suggested outcomes provide the most suitable parameter ranges based on their target preferences.
Event(s)
33rd International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2024, Taichung, 23 June 2024 through 26 June 2024. Code 32394
Subjects
Decision tree
Multi-objective decision tree
Process parameter
SDGs
Publisher
Springer Nature Switzerland
Type
conference paper