Open AccessStackelberg game approach to a bi-objective robust design optimization
Author(s) -
Li Dai,
Mengyuan Tang,
Sangmun Shin
Publication year - 2021
Publication title -
revista internacional de métodos numéricos para cálculo y diseño en ingeniería
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.213
H-Index - 9
eISSN - 1886-158X
pISSN - 0213-1315
DOI - 10.23967/j.rimni.2021.09.008
Subject(s) - stackelberg competition , mathematical optimization , dual (grammatical number) , computer science , robust optimization , optimization problem , process (computing) , response surface methodology , decomposition , quality (philosophy) , mathematics , machine learning , art , ecology , philosophy , literature , mathematical economics , epistemology , biology , operating system
Robust design has received a great deal of attention from quality researchers in recent years, and a number of optimization methodologies based on the dual response format have been proposed. The majority of existing bi-objective optimization models concentrate on the trade-offs between the process mean and variability functions without investigating the interactions between control factors and quality characteristics. The primary objective of this research is to integrate the Stackelberg leadership model into the robust design procedure and propose a Stackelberg game-based robust design (SGRD) method to determine appropriate control factor settings by minimizing the values of desired optimization targets based on an analysis of possible combinations of input and output quality parameters. Herein, first, a bi-objective robust design optimization problem is formulated as a dual response model using response surface methodology (RSM). Second, the proposed SGRD model is developed via decomposition into two leader-follower game models. Finally, the mean square error (MSE) criterion is applied to evaluate models, and select non-dominated solutions in various situations. Numerical examples are used to demonstrate that the proposed method provides significant solutions in cases containing unidentified priorities between the dual responses and undiscovered correlations among several inputs and outcomes. In addition, according to the case study analysis, the proposed method is more efficient than the conventional dual response approach when dealing with bi-objective robust design optimization problems.