Enhanced multi-objective solution approach for multiple quality characteristics optimisation problems considering predictive uncertainties

Abstract

Purpose The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple “quality characteristics” (or “responses”), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-II (NSGA-II), which improves the solution quality for multiple response optimisation (MRO) problems. Design/methodology/approach The proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions. Findings Implementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-II largely outperforms those of two existing MOO solution strategies. Research limitations/implications The enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces. Practical implications The best-ranked solutions according to the proposed approach are derived considering the model predictive uncertainties and MCDM technique. These solutions (or process setting conditions) are expected to be more reliable for satisfying customer specification compared to point estimate-based MOO solutions in real-life implementation. Originality/value No evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

Multi-objective optimization of safety instrumented systems maintenance strategy: a case study

“…Among optimization problems, multi-objective optimization (MOP) is commonly used in the multiple cases, for example, the design of complex hardware/software systems such as emergency shutdown systems (ESD), etc. The design costs of such systems often need to be minimized, while seeking maximum performance (Gan et al , 2017; Wang et al , 2017; Yang et al , 2018; Bram and Philip, 2019; Umamaheswari et al , 2018; Sharma and Mukherjee, 2019).…”

A robust multiobjective solution approach for mean-variance optimisation of correlated multiple quality characteristics

“…He et al (2021) proposed an interactive desirability function and simultaneous CI to resolve mean-variance MRO problems. In the second category [multiobjective optimisation (MOO)-based solution] Sharma and Mukherjee (2020) propose a MOO-based solution approach considering response uncertainties for mean-variance MRO problems. They recommend the use of the simultaneous PI of responses to resolve MRO problems.…”

“…However, researchers generally suggest simultaneous PIs as more precise interval estimations than simultaneous CIs to address responses uncertainties for correlated responses (Meeker et al , 2017; Montgomery and Peck, 1992). Finally, Sharma and Mukherjee (2020) illustrated the use of simultaneous PIs to determine feasible nondominated solutions for mean-variance MRO problems. However, they do not consider model parameter uncertainties, setting condition sensitivities, and response correlation strength to pick an efficient, robust front.…”

“…A critical review of the literature reveals that very few researchers have analysed RS model uncertainties and their influence on the quality of MRO solutions (Bera and Mukherjee, 2018;Chapman et al, 2014a;He et al, 2012He et al, , 2017Sharma and Mukherjee, 2020). Ignoring response surface model uncertainties when deriving efficient trade-off solutions may lead to considerable deviations in the expected and actual values of the responses for a given process setting condition.…”

“…MRO solutions also depend upon a consideration of only means or mean-variance of responses. Readers may refer to solution approaches proposed for mean MRO problems (Carlyle et al , 2000; Chapman et al , 2014a; Derringer, 1994) and mean-variance MRO problems (Bera and Mukherjee, 2013; Kim and Lin, 2006; Kovach et al , 2009; Sharma and Mukherjee, 2020). The mean-variance MRO problem complexity is higher than a mean MRO problem (Lee et al , 2018).…”