Robust parameter design of dynamic multi‐response system: a new integrated method

Abstract

Purpose -The purpose of this paper is to propose a new method of robust parameter design for dynamic multi-response system. The objectives are to resolve the correlations among multiple responses and the uncertainty of system with incomplete information. Design/methodology/approach -First, desirability function is used to measure dynamic system sensitivity and system variation, and principal component analyses on the two indices are conducted. Second, the grey relational grade (GRD) between principal component sequences of the two indices and their respective ideal sequences, gained by grey relational analysis, is converted to an integrated GRD (IGRD) index by means of TOPSIS method, and then the optimal level combination of controllable factors is identified based on the IGRD index. Findings -It was found that the optimal factor level combination obtained by the proposed method is nearest the ideal solution and farthest from the negative ideal solution. The validity and superiority of the proposed method are confirmed through two illustrative examples. Research limitations/implications -It should be noted that the proposed method fails to consider the interaction effects between controllable factors and noise factors. Originality/value -The method proposed in the paper effectively integrates several common methods to optimize a dynamic multiple responses system based on Taguchi's robust parameter design. These methods do not involve complicated mathematical theory, and are therefore easy for practitioners to use in engineering practice.

System dynamics model for intra-city multimodal transportation considering behavioral indicators and demand under uncertainty conditions

“…The results showed that the SD model can be an impressive tool in outlining the short-term and long-term implications of planned strategies. Wang et al (2011) proposed a new robust parameter design method for a dynamic multi-response system to resolve the correlations among multiple responses and the uncertainty of the system with incomplete information. Tierney et al (2014) used the integer-programming model for interterminal transportation (ITT) analysis to develop seaports.…”

An Integrated Approach to Correlated Multi-Response Optimization

“…Second, the correlation, goal conflict and global optimization of multiple responses are still very difficult to resolve simultaneously. Third, a general limitation of many mentioned approaches is that they can only find the discrete optimal parameters setting [10]. In the paper, an integrated approach based on statistical methods and heuristic optimization algorithms is proposed to overcome these shortcomings.…”

Optimization of multi-response dynamic systems integrating multiple regression and Taguchi’s dynamic signal-to-noise ratio concept

“…Industries have, therefore, all the time more highlighted building up measures competent of concurrently optimizing the multi-response dynamic systems in the light of the increasing complexity of modern product design. To deal with the requirements of the contemporary industries, more than a few studies have (Tong et al, 2002;Tong et al, 2004;Hsieh et al, 2005;Wang and Tong, 2005;Wu and Yeh, 2005;Chang, 2006;Wang, 2007;Wu, 2007;Chang, 2008;Tong et al, 2008;Wu, 2009;Zhong and Liu, 2009;Chang and Chen, 2011;Wang et al, 2011;Sharma et al, 2012;Gauri, 2014;Zhang et al, 2014;Wu, 2015;He et al, 2015;Bashiri et al, 2016) have proposed different procedures for optimizing a multi-response dynamic system. Applying the two-step procedure is not feasible for optimizing a multi-response dynamic system because it is almost impossible to find out any control factor that may be considered as an adjustment factor.…”