• Title, Summary, Keyword: response surface analysis

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The Optimization of Bank Branches Efficiency by Means of Response Surface Method and Data Envelopment Analysis: A Case of Iran

  • Shadkam, Elham;Bijari, Mehdi
    • The Journal of Asian Finance, Economics, and Business
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    • v.2 no.2
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    • pp.13-18
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    • 2015
  • In this paper the DRC model is presented for solving multi objective problem. The proposed model is a combination of data envelopment analysis, Cuckoo algorithm and the response surface method. Due to reasons like costs, time and irreversible damages, it is not possible to analyze each and every one of the proposed models in practice, so the simulation is used. Since the number of experiments for simulation process is high then the optimization has gone to practice and directs the simulation process. The response surface method is used as one of the approaches of simulation optimization. Furthermore, data envelopment analysis is used to consider several response surfaces as efficiency response surface. Then this efficiency response surface is solved by Cuckoo algorithms. The main advantage of DRC model is to make one efficiency response surface function instate of multi surface function for every output and also using the advantages of Cuckoo algorithms. In order to demonstrate the effectiveness of the proposed approach, the branches of Refah bank in Mashhad is analyzed and the results are presented.

A new high-order response surface method for structural reliability analysis

  • Li, Hong-Shuang;Lu, Zhen-Zhou;Qiao, Hong-Wei
    • Structural Engineering and Mechanics
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    • v.34 no.6
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    • pp.779-799
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    • 2010
  • In order to consider high-order effects on the actual limit state function, a new response surface method is proposed for structural reliability analysis by the use of high-order approximation concept in this study. Hermite polynomials are used to determine the highest orders of input random variables, and the sampling points for the determination of highest orders are located on Gaussian points of Gauss-Hermite integration. The cross terms between two random variables, only in case that their corresponding percent contributions to the total variation of limit state function are significant, will be added to the response surface function to improve the approximation accuracy. As a result, significant reduction in computational cost is achieved with this strategy. Due to the addition of cross terms, the additional sampling points, laid on two-dimensional Gaussian points off axis on the plane of two significant variables, are required to determine the coefficients of the approximated limit state function. All available sampling points are employed to construct the final response surface function. Then, Monte Carlo Simulation is carried out on the final approximation response surface function to estimate the failure probability. Due to the use of high order polynomial, the proposed method is more accurate than the traditional second-order or linear response surface method. It also provides much more efficient solutions than the available high-order response surface method with less loss in accuracy. The efficiency and the accuracy of the proposed method compared with those of various response surface methods available are illustrated by five numerical examples.

A response surface method based on sub-region of interest for structural reliability analysis

  • Zhao, Weitao;Shi, Xueyan;Tang, Kai
    • Structural Engineering and Mechanics
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    • v.57 no.4
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    • pp.587-602
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    • 2016
  • In structural reliability analysis, the response surface method is widely adopted because of its numerical efficiency. It should be understood that the response function must approximate the actual limit state function accurately in the main region influencing failure probability where it is evaluated. However, the size of main region influencing failure probability was not defined clearly in current response surface methods. In this study, the concept of sub-region of interest is constructed, and an improved response surface method is proposed based on the sub-region of interest. The sub-region of interest can clearly define the size of main region influencing failure probability, so that the accuracy of the evaluation of failure probability is increased. Some examples are introduced to demonstrate the efficiency and the accuracy of the proposed method for both numerical and implicit limit state functions.

Improved Response Surface Method Using Modified Selection Technique of Sampling Points (개선된 평가점 선정기법을 이용한 응답면기법)

  • 김상효;나성원;황학주
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • pp.248-255
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    • 1993
  • Recently, due to the increasing attention to the structural safety under uncertain environments, many researches on the structural reliability analysis have been peformed. Some useful methods are available to evaluate performance reliability of structures with explicit limit states. However, for large structures, in which structural behaviors can be analyzed with finite element models and the limit states are only expressed implicitly, Monte-Carlo simulation method has been mainly used. However, Monte-Carlo simulation method spends too much computational time on repetitive structural analysis. Many alternative methods are suggested to reduce the computational work required in Monte-Carlo simulation. Response surface method is widely used to improve the efficiency of structural reliability analysis. Response surface method is based on the concept of approximating simple polynomial function of basic random variables for the limit state which is not easily expressed in explicit forms of design random variables. The response surface method has simple algorithm. However, the accuracy of results highly depends on how properly the stochastic characteristics of the original limit state has been represented by approximated function, In this study, an improved response surface method is proposed in which the sampling points for creating response surface are modified to represent the failure surface more adequately and the combined use of a linear response surface function and Rackwitz-Fiessler method has been employed. The method is found to be more effective and efficient than previous response surface methods. In addition more consistent convergence is achieved, Accuracy of the proposed method has been investigated through example.

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Response Surface Methodology Using a Fullest Balanced Model: A Re-Analysis of a Dataset in the Korean Journal for Food Science of Animal Resources

  • Rheem, Sungsue;Rheem, Insoo;Oh, Sejong
    • Food Science of Animal Resources
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    • v.37 no.1
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    • pp.139-146
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    • 2017
  • Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. In the analysis of response surface data, a second-order polynomial regression model is usually used. However, sometimes we encounter situations where the fit of the second-order model is poor. If the model fitted to the data has a poor fit including a lack of fit, the modeling and optimization results might not be accurate. In such a case, using a fullest balanced model, which has no lack of fit, can fix such problem, enhancing the accuracy of the response surface modeling and optimization. This article presents how to develop and use such a model for the better modeling and optimizing of the response through an illustrative re-analysis of a dataset in Park et al. (2014) published in the Korean Journal for Food Science of Animal Resources.

Optimization of Chassis Frame by Using D-Optimal Response Surface Model (D-Optimal 반응표면모델에 의한 섀시 프레임 최적설치)

  • Lee, Gwang-Gi;Gu, Ja-Gyeom;Lee, Tae-Hui
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.24 no.4
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    • pp.894-900
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    • 2000
  • Optimization of chassis frame is performed according to the minimization of eleven responses representing one total frame weight, three natural frequencies and seven strength limits of chassis frame that are analyzed by using each response surface model from D-optimal design of experiments. After each response surface model is constructed form D-optimal design and random orthogonal array, the main effect and sensitivity analyses are successfully carried out by using this approximated regression model and the optimal solutions are obtained by using a nonlinear programming method. The response surface models and the optimization algorithms are used together to obtain the optimal design of chassis frame. The eleven-polynomial response surface models of the thirteen frame members (design factors) are constructed by using D-optimal Design and the multi-disciplinary design optimization is also performed by applying dual response analysis.

Optimization of Cometabolic Trichloroethylene Degradation Conditions by Response Surface Analysis (반응표면 분석법을 이용한 트리클로로에틸렌의 공대사적 분해조건 최적화)

  • 윤성준
    • KSBB Journal
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    • v.15 no.4
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    • pp.393-397
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    • 2000
  • The cometaboic biodegradation conditionso f trichloroethylene(TCE) by Burkholderia cepacia G4 were optimized using response surface analysis. The experimental sets of phenol concentration temperature and pH were designed using central composite experimental design. The optimal conditions of phenol concentration temperature and pH were determined to be 0.91 ppm 21.5$^{\circ}C$ and 7.65 respectively by the Ridge analysis of the contour plot for TCE biodegradation rates. The TCE biodegradation rate could be enhanced up to 2.43 nmol.mg protein$.$min by response surface methodology.

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Statistical Analysis and Prediction for Behaviors of Tracked Vehicle Traveling on Soft Soil Using Response Surface Methodology (반응표면법에 의한 연약지반 차량 거동의 통계적 분석 및 예측)

  • Lee Tae-Hee;Jung Jae-Jun;Hong Sup;Km Hyung-Woo;Choi Jong-Su
    • Journal of Ocean Engineering and Technology
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    • v.20 no.3
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    • pp.54-60
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    • 2006
  • For optimal design of a deep-sea ocean mining collector system, based on self-propelled mining vehicle, it is imperative to develop and validate the dynamic model of a tracked vehicle traveling on soft deep seabed. The purpose of this paper is to evaluate the fidelity of the dynamic simulation model by means of response surface methodology. Various statistical techniques related to response surface methodology, such as outlier analysis, detection of interaction effect, analysis of variance, inference of the significance of design variables, and global sensitivity analysis, are examined. To obtain a plausible response surface model, maximum entropy sampling is adopted. From statistical analysis and prediction for dynamic responses of the tracked vehicle, conclusions will be drawn about the accuracy of the dynamic model and the performance of the response surface model.

Probabilistic determination of initial cable forces of cable-stayed bridges under dead loads

  • Cheng, Jin;Xiao, Ru-Cheng;Jiang, Jian-Jing
    • Structural Engineering and Mechanics
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    • v.17 no.2
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    • pp.267-279
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    • 2004
  • This paper presents an improved Monte Carlo simulation for the probabilistic determination of initial cable forces of cable-stayed bridges under dead loads using the response surfaces method. A response surface (i.e. a quadratic response surface without cross-terms) is used to approximate structural response. The use of the response surface eliminates the need to perform a deterministic analysis in each simulation loop. In addition, use of the response surface requires fewer simulation loops than conventional Monte Carlo simulation. Thereby, the computation time is saved significantly. The statistics (e.g. mean value, standard deviation) of the structural response are calculated through conventional Monte Carlo simulation method. By using Monte Carlo simulation, it is possible to use the existing deterministic finite element code without modifying it. Probabilistic analysis of a truss demonstrates the proposed method' efficiency and accuracy; probabilistic determination of initial cable forces of a cable-stayed bridge under dead loads verifies the method's applicability.

The Analysis of the Seepage Quantity of Reservoir Embankment using Stochastic Response Surface Method (확률론적 응답면 기법을 이용한 저수지 제체의 침투수량 해석)

  • Bong, Tae-Ho;Son, Young-Hwan;Noh, Soo-Kack;Choi, Woo-Seok
    • Journal of The Korean Society of Agricultural Engineers
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    • v.55 no.3
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    • pp.75-84
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    • 2013
  • The seepage quantity analysis of reservoir embankment is very important for assessment of embankment safety. However, the conventional analysis does not consider uncertainty of soil properties. Permeability is known that the coefficient of variation is larger than other soil properties and seepage quantity is highly dependent on the permeability of embankment. Therefore, probabilistic analysis should be carried out for seepage analysis. To designers, however, the probabilistic analysis is not an easy task. In this paper, the method that can be performed probabilistic analysis easily and efficiently through the numerical analysis based commercial program is proposed. Stochastic response surface method is used for approximate the limit state function and when estimating the coefficients, the moving least squares method is applied in order to reduce local error. The probabilistic analysis is performed by LHC-MCS through the response surface. This method was applied to two type (homogeneous, core zone) earth dams and permeability of embankment body and core are considered as random variables. As a result, seepage quantity was predicted effectively by response surface and probabilistic analysis could be successfully implemented.