Simultaneous prediction interval-based multiobjective solution approach for multiple quality characteristics optimization

Sharma, Abhinav Kumar; Mukheijee, Indrajit

doi:10.1109/iea.2018.8387127

Cited by 1 publication

(2 citation statements)

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“…They compared the Pareto front solution quality of the NSGA-II (Deb et al , 2002) and MOPSO (Coello et al , 2004) strategy for two different mean MRO problems. This research is an extension of their work (Sharma and Mukherjee, 2018) with the following new contributions: an enhanced MOO solution approach for both mean and mean-variance MRO problems is proposed; balanced mean and mean-variance MRO cases are considered (three mean MRO and three mean-variance MRO problems) to demonstrate the suitability of the proposed MOO approach; an efficient NSGA-II-TS R is proposed (which adopts clustering, the MD, and the Tabu move concept to balance the intensification and diversification scheme) to resolve any mean or mean-variance MRO problems; a systematic metric-based approach is provided (namely the MD and weighted mean square error (WMSE)) to determine the best implementable solutions (using the VIKOR or TOPSIS MCDM technique) for mean and mean-variance MRO problems, respectively.…”

Section: Literature Reviewmentioning

confidence: 89%

“…They proposed a two-stage efficient frontier approach based on user priorities. Sharma and Mukherjee (2018) proposed a simultaneous PI-based Pareto front solution strategy for mean MRO problems, considering RS uncertainties. The simultaneous PIs for all mean responses were constructed and compared with the specifications to direct the iterative search towards an efficient front.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Sharma

Mukherjee

2019

IJQRM

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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.

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Section: Literature Reviewmentioning

confidence: 89%

Section: Literature Reviewmentioning

confidence: 99%