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首页> 外文期刊>International Journal of Quality & Reliability Management >Enhanced multi-objective solution approach for multiple quality characteristics optimisation problems considering predictive uncertainties
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Enhanced multi-objective solution approach for multiple quality characteristics optimisation problems considering predictive uncertainties

机译：考虑到预测性不确定性，增强了多目标优化问题的多目标解决方法

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摘要

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.
机译：目的 - 本文的目的是解决三个关键目标。首先是提高多目标优化（MOO）解决方案方法的均值和平均方差优化多种“质量特征”（或“响应”），考虑到预测的不确定性。第二个目标是将建议方法与现有方法的解决方案质量进行比较。第三个目的是修饰的非主导分类遗传算法-II（NSGA-II）的提议，其提高了多重响应优化（MRO）问题的溶液质量。设计/方法/方法 - 所提出的解决方案方法集成了经验响应表面（RS）模型，同时预测间隔的MOO迭代搜索和多标准决策（MCDM）技术，以选择最佳可实现的有效解决方案。调查结果 - 各种各样的MRO问题中所提出的方法的实施表明，在最坏情况场法中，解决方案质量的显着提高。此外，结果表明，改性NSGA-II的溶液质量主要优于两个现有的MOO解决方案策略。研究限制/影响 - 增强的MOO解决方案方法仅限于参数RS预测模型和连续搜索空间。考虑到模型预测不确定性和MCDM技术，导出了根据所提出的方法的最佳排名解决方案。这些解决方案（或过程设定条件）预计与现实生活中的基于点估计的MOO解决方案相比，可以更加可靠。与现实生活中的点估计的MOO解决方案相比，这些解决方案。重要/价值 - 没有证明早期研究的证据表明了适合性和优越性考虑到RS不确定性，MAO解决方案方法均为均值和平均方差MRO问题。此外，这项工作说明了六个所选MRO问题的提出方法的逐步实现结果。

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