Trade-off in robustness, cost and performance by a multi-objective robust production optimization method

Parnianifard, Amir; Azfanizam, A. S.; Ariffin, Mohd Khairol Anuar Mohd; Ismail, Mohd Idris Shah

doi:10.5267/j.ijiec.2018.2.001

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…Other surrogates such as ANN, RBF, polynomial regression, and polynomial chaos expansion can be combined with also some other common metaheuristics such as NSGAII, PSO, ACO, and GA. Instead of conventional robust dual surface, recent developments in robust optimization approaches may be served to tackle the existence of uncertainty in the model, see 39 , 40 , 97 , 98 . The proposed approach can manage to consider other uncertainty distributions e.g., Gaussian among the crossed array framework in addition of uniform distribution in this paper, see 72 for such cases with an unknown probability distribution of uncertainty.…”

Section: Discussionmentioning

confidence: 99%

A data driven approach in less expensive robust transmitting coverage and power optimization

Parnianifard

Mumtaz

Chaudhary

et al. 2022

Sci Rep

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This paper aims the development of a new reduced-cost algorithm for a multi-objective robust transmitter placement under uncertainty. Toward this end, we propose a new hybrid Kriging/Grey Wolf Optimizer (GWO) approach combined with robust design optimization to estimate the set of Pareto frontier by searching robustness as well as accuracy (lower objective function) in a design space. We consider minimization of the energy power consumption for transmitting as well as maximization of signal coverage in a multi-objective robust optimization model. The reliability of the model to control signal overlap for multiple transmitting antennas is also provided. To smooth computational cost, the proposed method instead of evaluating all receiver test points in each optimization iteration approximates signal coverages using Kriging interpolation to obtain optimal transmitter positions. The results demonstrate the utility and the efficiency of the proposed method in rendering the robust optimal design and analyzing the sensitivity of the transmitter placement problem under practically less-expensive computational efforts (350% and 320% less than computational time elapsed using standalone GWO and NSGAII respectively).

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Section: Discussionmentioning

confidence: 99%

A data driven approach in less expensive robust transmitting coverage and power optimization

Parnianifard

Mumtaz

Chaudhary

et al. 2022

Sci Rep

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“…Only if the variation of the production attribute obtained through parameter design is unsatisfactory is tolerance design implemented. In general, statistical analysis techniques used in uncertainty quantification can be classified to i) Model-based or simulation-based design using Monte-Carlo experiments [37], [38], robust design [39], [40], Bayesian inference method [41], [42], Taguchi parameter design [43], [44], reliabilitybased design optimization [45], [46] Box and Wilson [70] searches for the set of continuous inputs that reduces the single output of a real-world system (or maximizes that output). In comprehensive surveys in the literature, such as [28], [71], [72], numerous applications of PR are discussed.…”

Section: Related Workmentioning

confidence: 99%

Expedited Surrogate-Based Quantification of Engineering Tolerances using A Modified Polynomial Regression

Parnianifard

Chaudhary

Mumtaz

et al. 2023

Preprint

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Statistical analysis is frequently used to determine how manufacturing tolerances or operating condition uncertainties affect system performance. Surrogate is one of the accelerating ways in engineering tolerance quantification to analyze uncertainty with an acceptable computational burden rather than costly traditional methods such as Monte Carlo simulation. Compared with more complicated surrogates such as the Gaussian process, or Radial Basis Function (RBF), the Polynomial Regression (PR) provides simpler formulations yet acceptable outcomes. However, PR with the common least-squares method needs to be more accurate and flexible for approximating nonlinear and nonconvex models. In this study, a new approach is proposed to enhance the accuracy and approximation power of PR in dealing with uncertainty quantification in engineering tolerances. For this purpose, first, by computing the differences between training sample points and a reference point (e.g., nominal design), we employ certain linear and exponential basis functions to transform an original variable design into new transformed variables. A second adjustment is made to calculate the bias between the true simulation model and the surrogate's approximated response. To demonstrate the effectiveness of the proposed PR approach, we provide comparison results between conventional and proposed surrogates employing four practical problems with geometric fabrication tolerances such as three-bar truss design, welded beam design, and trajectory planning of two-link and three-link (two and three degrees of freedom) robot manipulator. The obtained results prove the preference of the proposed approach over conventional PR by improving the approximation accuracy of the model with significantly lower prediction errors.

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“…In [7] and [8] has recommended that in the case of LTB the magnitude of 𝛼𝛼 needs to significantly greater than one but not necessarily a large number or infinity, and they suggested 𝛼𝛼 = 2 is appropriate to be employed in practice. In [9], the application of Eq. ( 4) has been expanded in multi-objective engineering design problems.…”

Section: Quality Loss Function (Qlf)mentioning

confidence: 99%

Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems

Parnianifard¹

2022

Preprint

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One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers’ viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.

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Trade-off in robustness, cost and performance by a multi-objective robust production optimization method

Cited by 9 publications

References 29 publications

A data driven approach in less expensive robust transmitting coverage and power optimization

A data driven approach in less expensive robust transmitting coverage and power optimization

Expedited Surrogate-Based Quantification of Engineering Tolerances using A Modified Polynomial Regression

Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems

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