Journal of Quality Engineering and Management

A semi-parametric method for optimizing multi-response problems: A case study on improving the quality of plastic injection machines

Document Type : Original Article

Authors

Mehran Tavakoli 1
Mohammad Bamenimoghadam 2

1 Researcher of Social and Economic Statistics, Allameh Tabatabai University, Tehran, Iran

2 Allameh Tabatabaei University

Abstract
Multi-response optimization performed by the response procedure method is very common. Before optimization, we need to select and fit the appropriate model for each response. A major problem that may occur due to incorrect fitting of models and failure to reach optimal solutions is model misidentification. The solid regression model method, which is a semi-parametric method for estimating d, can have a better performance than both parametric and non-parametric estimation methods against model misclassification. In this research, the use of a robust regression model method is proposed to improve the model estimation and the appropriate fit of each of the answers will be investigated by one of the multivariate optimization methods, namely the utility function. In the following, an applied study is presented to compare parametric, nonparametric and semi-parametric methods. The results of this study show that the performance of the stable regression model is more appropriate in many situations as well as in the modeling stage than the other two methods. Therefore, the optimization results with a stable regression model are much more reliable.

Keywords

Response Procedure Methodology
Multi-response optimization
Utility function
Semi-parametric method
Stable regression model
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Journal of Quality Engineering and Management
Volume 7, Issue 1
Spring 2017
Pages 16-28

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