Response surface approach to robust design of assembly cells through simulation

2018 ◽  
Vol 38 (4) ◽  
pp. 450-464 ◽  
Author(s):  
Cem Savas Aydin ◽  
Senim Ozgurler ◽  
Mehmet Bulent Durmusoglu ◽  
Mesut Ozgurler
Keyword(s):  
Response Surface ◽  
Robust Design ◽  
Performance Measures ◽  
Performance Optimization ◽  
Design Approach ◽  
Shop Floor ◽  
Optimization Approach ◽  
Content Type ◽  
Performance Variability ◽  
Noise Factors

Purpose This paper aims to present a multi-response robust design (RD) optimization approach for U-shaped assembly cells (ACs) with multi-functional walking-workers by using operational design (OD) factors in a simulation setting. The proposed methodology incorporated the design factors related to the operation of ACs into an RD framework. Utilization of OD factors provided a practical design approach for ACs addressing system robustness without modifying the cell structure. Design/methodology/approach Taguchi’s design philosophy and response surface meta-models have been combined for robust simulation optimization (SO). Multiple performance measures have been considered for the study and concurrently optimized by using a multi-response optimization (MRO) approach. Simulation setting provided flexibility in experimental design selection and facilitated experiments by avoiding cost and time constraints in real-world experiments. Findings The present approach is illustrated through RD of an AC for performance measures: average throughput time, average WIP inventory and cycle time. Findings are in line with expectations that a significant reduction in performance variability is attainable by trading-off optimality for robustness. Reductions in expected performance (optimality) values are negligible in comparison to reductions in performance variability (robustness). Practical implications ACs designed for robustness are more likely to meet design objectives once they are implemented, preventing changes or roll-backs. Successful implementations serve as examples to shop-floor personnel alleviating issues such as operator/supervisor resistance and scepticism, encouraging participation and facilitating teamwork. Originality/value ACs include many activities related to cell operation which can be used for performance optimization. The proposed framework is a realistic design approach using OD factors and considering system stochasticity in terms of noise factors for RD optimization through simulation. To the best of the authors’ knowledge, it is the first time a multi-response RD optimization approach for U-shaped manual ACs with multi-functional walking-workers using factors related to AC operation is proposed.

scholarly journals A robust multi response surface approach for optimization of multistage processes

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amir Moslemi ◽  
Mahmood Shafiee
Keyword(s):  
Robust Optimization ◽  
Response Surface ◽  
Numerical Study ◽  
Ordinary Least Squares ◽  
Response Surfaces ◽  
Estimation Methods ◽  
Design Parameters ◽  
Optimization Approach ◽  
Content Type

PurposeIn a multistage process, the final quality in the last stage not only depends on the quality of the task performed in that stage but is also dependent on the quality of the products and services in intermediate stages as well as the design parameters in each stage. One of the most efficient statistical approaches used to model the multistage problems is the response surface method (RSM). However, it is necessary to optimize each response in all stages so to achieve the best solution for the whole problem. Robust optimization can produce very accurate solutions in this case.Design/methodology/approachIn order to model a multistage problem, the RSM is often used by the researchers. A classical approach to estimate response surfaces is the ordinary least squares (OLS) method. However, this method is very sensitive to outliers. To overcome this drawback, some robust estimation methods have been presented in the literature. In optimization phase, the global criterion (GC) method is used to optimize the response surfaces estimated by the robust approach in a multistage problem.FindingsThe results of a numerical study show that our proposed robust optimization approach, considering both the sum of square error (SSE) index in model estimation and also GC index in optimization phase, will perform better than the classical full information maximum likelihood (FIML) estimation method.Originality/valueTo the best of the authors’ knowledge, there are few papers focusing on quality-oriented designs in the multistage problem by means of RSM. Development of robust approaches for the response surface estimation and also optimization of the estimated response surfaces are the main novelties in this study. The proposed approach will produce more robust and accurate solutions for multistage problems rather than classical approaches.

Determining quantitative targets for performance measures in the balanced scorecard method using response surface methodology

2018 ◽  
Vol 56 (9) ◽  
pp. 2006-2037 ◽  
Author(s):  
Sorour Farokhi ◽  
Emad Roghanian
Keyword(s):  
Response Surface Methodology ◽  
Performance Evaluation ◽  
Response Surface ◽  
Performance Measures ◽  
Balanced Scorecard ◽  
Decision Makers ◽  
Content Type ◽  
Strategic Objectives ◽  
Strategy Map ◽  
Optimal Values

Purpose The purpose of this paper is to propose a quantitative methodology for setting targets in the framework of Balanced Scorecard (BSC) in order to achieve vision and goals. Design/methodology/approach Response Surface Methodology is proposed to find the significant relationships that should be included in the strategy map and the optimal values of performance measures are assessed by using the desirability function-based approach of RSM. The proposed method was created by reviewing the existing literature, modeling the problem, and applying it in an oil company. In fact, RSM is used to execute the design matrix, analyze the collected data, extract models, analyze the results, and optimize the procedures that generate multiple responses. Findings By applying this methodological design, a clearer picture of the relationships between strategic objectives is obtained and the influence of strategic objectives on one another is determined. Afterward, optimal values for performance measures are determined. Research limitations/implications This paper proposes a framework for constructing a strategy map and setting quantitative targets to translate the goals and strategies into corresponding performance measures and targets. Also, this paper presents a case study to demonstrate the applicability and effectiveness of the proposed approach. However, RSM-based techniques require a greater amount of data to generate more accurate results. Although the advent of the Information Age has forced organizations’ decision makers to provide sufficient information and data for business analysis, the data requirements of RSM-based techniques are met. Practical implications In practice, the process of setting targets for performance measures can be challenging in terms of reaching a consensus between managers and decision makers. The findings of this paper can offer a new approach for performance evaluation based on the BSC which allows the organization’s decision makers to reach a more accurate picture of the relationship model between organization goals and those objectives within the BSC. It also demonstrates how decision makers can be guided in the process of defining performance target values in the BSC method. Originality/value Reviewing the literature on setting quantitative targets within the framework of the BSC showed no prior study in which RSM is used. This approach has two main contributions: the associations among strategic objectives are investigated and obtained in an effective way which analytically identifies the direction and degree of the relations among the performance measures. Considering the performance evaluation structure based on the BSC, quantitative targets have been determined to help in achieving the long-term goals of the organization. The application of the proposed method in a company showed that the contributions of this research are not only theoretical, but practical as well.

Application of the corrected response surface methodology for the optimization of a permanent magnet synchronous machine

2018 ◽  
Vol 37 (2) ◽  
pp. 740-754 ◽  
Author(s):  
Stéphane Vivier
Keyword(s):  
Optimal Design ◽  
Permanent Magnet ◽  
Response Surface ◽  
Synchronous Machine ◽  
Optimization Techniques ◽  
Permanent Magnet Synchronous Machine ◽  
Optimization Approach ◽  
Content Type ◽  
Coarse Model ◽  
Conventional Optimization

PurposeThis paper aims to introduce an original application of the corrected response surface method (CRSM) in the context of the optimal design of a permanent magnet synchronous machine used as an integrated starter generator. This method makes it possible to carry out this design in a very efficient manner, in comparison with conventional optimization approaches. Design/methodology/approachThe search for optimal conditions is achieved by the joint use of two multi-physics models of the machine to be optimized. The former models most finely the physical functioning of the machine; it is called “fine model”. The second model describes the same physical phenomena as the fine model but must be much quicker to evaluate. Thus, to minimize its evaluation time, it is necessary to simplify it considerably. It is called “coarse model”. The lightness of the coarse model allows it to be used intensively by conventional optimization algorithms. On the other hand, the fine reference model makes it possible to recalibrate the results obtained from the coarse model at any instant, and mainly at the end of each classical optimization. The difference in definition between fine and coarse models implies that these two models do not give the same output values for the same input configuration. The approach described in this study proposes to correct the values of the coarse model outputs by constructing an adjustment (correcting) response surface. This gives the name to this method. It then becomes possible to have the entire load of the optimization carried over to the coarse model adjusted by the addition of this correction response surface. FindingsThe application of this method shows satisfactory results, in particular in comparison with those obtained with a traditional optimization approach based on a single (fine) model. It thus appears that the approach by CRSM makes it possible to converge much more quickly toward the optimal configurations. Also, the use of response surfaces for optimization makes it possible to capitalize the modeling data, thus making it possible to reuse them, if necessary, for subsequent optimal design studies. Numerous tests show that this approach is relatively robust to the variations of many important functioning parameters. Originality/valueThe CRSM technique is an indirect multi-model optimization method. This paper presents the application of this relatively undeveloped optimization approach, combining the features and benefits of (Indirect) efficient global optimization techniques and (multi-model) space mapping methods.

Manual spray painting process optimization using Taguchi robust design

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Noura Almansoori ◽  
Samah Aldulaijan ◽  
Sara Althani ◽  
Noha M. Hassan ◽  
Malick Ndiaye ◽  
...  
Keyword(s):  
Human Factors ◽  
Robust Design ◽  
Surface Defects ◽  
Cost Model ◽  
Industrial Robots ◽  
Content Type ◽  
Model Response ◽  
Optimal Setting ◽  
The Cost ◽  
Noise Factors

PurposeResearchers heavily investigated the use of industrial robots to enhance the quality of spray-painted surfaces. Despite its advantages, automating process is not always economically feasible. The manual process, on the other hand, is cheaper, but its quality is prone to the mental and physical conditions of the worker making it difficult to operate consistently. This research proposes a mathematical cost model that integrates human factors in determining optimal process settings.Design/methodology/approachTaguchi's robust design is used to investigate the effect of fatigue, stability of worker's hand and speed on paint consumption, surface quality, and processing time. A crossed array experimental design is deployed. Regression analysis is then used to model response variables and formulate cost model, followed by a multi-response optimization.FindingsResults reveal that noise factors have a significant influence on painting quality, time, and cost of the painted surface. As a result, a noise management strategy should be implemented to reduce their impact and obtain better quality and productivity results. The cost model can be used to determine optimal setting for different applications by product and by industry.Originality/valueHardly any research considered the influence of human factors. Most focused on robot trajectory and its effect on paint uniformity. In proposed research, both cost and quality are integrated into a single objective. Quality is measured in terms of uniformity, smoothness, and surface defects. The interaction between trajectory and flow rate is investigated here for the first time. A unique approach integrating quality management, statistical analysis, and optimization is used.

scholarly journals Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design

2017 ◽  
Vol 37 (2) ◽  
pp. 89-98
Author(s):  
Enrique Canessa ◽  
Sergio Chaigneau
Keyword(s):  
Genetic Algorithm ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Robust Design ◽  
Variance Reduction ◽  
Missing Values ◽  
Efficient Solutions ◽  
Target Values ◽  
Response Systems ◽  
Noise Factors

We present an improved Pareto Genetic Algorithm (PGA), which finds solutions to problems of robust design in multi-response systems with 4 responses and as many as 10 control and 5 noise factors. Because some response values might not have been obtained in the robust design experiment and are needed in the search process, the PGA uses Response Surface Methodology (RSM) to estimate them. Not only the PGA delivered solutions that adequately adjusted the response means to their target values, and with low variability, but also found more Pareto efficient solutions than a previous version of the PGA. This improvement makes it easier to find solutions that meet the trade-off among variance reduction, mean adjustment and economic considerations. Furthermore, RSM allows estimating outputs’ means and variances in highly non-linear systems, making the new PGA appropriate for such systems.

Robust Design of UOE Forming Process Based on Support Vector Machine

2015 ◽  
Vol 817 ◽  
pp. 523-530
Author(s):  
Tian Xia Zou ◽  
Guang Han Wu ◽  
Da Yong Li ◽  
Qiang Ren ◽  
Ying Hong Peng
Keyword(s):  
Support Vector Machine ◽  
Robust Optimization ◽  
Robust Design ◽  
Pipeline Steel ◽  
Optimization Methods ◽  
Material Behavior ◽  
Support Vector ◽  
Optimization Approach ◽  
Forming Process ◽  
Noise Factors

Fluctuations in material properties of the incoming steel for UOE forming process are widespread. According to the statistics, the fluctuation range of the yield strength of the same grade pipeline steel is around 80MPa. Robust optimization methods have been widely applied in sheet metal forming area. In this paper, experiments were conducted to investigate how a stochastic material behavior of noise factors affected UOE forming quality. Robust design models integrated with response surface method for UOE forming process were established to minimize impact of the variations and improve the qualified rate of UOE pipe ovality. Support vector machine in both classification and regression was adopted to map the relation between input process parameters and forming qualities. The deterministic and robust optimization results are presented and compared, demonstrating increased process robustness and decreased number of product rejects by application of the robust optimization approach.

scholarly journals Integration of the Response Surface Methodology With the Compromise Decision Support Problem in Developing a General Robust Design Procedure

1995 ◽  
Author(s):  
Wei Chen ◽  
Kwok-Leung Tsui ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Decision Support ◽  
Response Surface ◽  
Robust Design ◽  
Irrigation System ◽  
Design Procedure ◽  
Closed Form Solutions ◽  
Control Factors ◽  
Design Variables ◽  
Solar Powered ◽  
Noise Factors

Abstract In this paper we introduce a comprehensive and rigorous robust design procedure to overcome some limitations of the current approaches. A comprehensive approach is general enough to model the two major types of robust design applications, namely, • robust design associated with the minimization of the deviation of performance caused by the deviation of noise factors (uncontrollable parameters), AND • robust design due to the minimization of the deviation of performance caused by the deviation of control factors (design variables). We achieve mathematical rigor by using, as a foundation, principles from the design of experiments and optimization. Specifically, we integrate the Response Surface Method (RSM) with the compromise Decision Support Problem (DSP). Our approach is especially useful for design problems where there are no closed-form solutions and system performance is computationally expensive to evaluate. The design of a solar powered irrigation system is used as an example. Our focus in this paper is on illustrating our approach rather than on the results per se.

scholarly journals Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

2021 ◽  
Vol 11 (15) ◽  
pp. 6768
Author(s):  
Tuan-Ho Le ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Response Surface Methodology ◽  
Transfer Function ◽  
Response Surface ◽  
Robust Design ◽  
Transfer Functions ◽  
Desirability Function ◽  
Likelihood Estimation ◽  
Statistical Simulation ◽  
Screening Procedure ◽  
Function Estimation

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

Good problem solvers? Leveraging knowledge sharing mechanisms and management support

2019 ◽  
Vol 23 (6) ◽  
pp. 1017-1038 ◽  
Author(s):  
Ambra Galeazzo ◽  
Andrea Furlan
Keyword(s):  
Transformational Leadership ◽  
Problem Solving ◽  
Knowledge Sharing ◽  
Hierarchical Linear Modeling ◽  
Shop Floor ◽  
Management Support ◽  
Linear Modeling ◽  
Content Type ◽  
Problem Solvers ◽  
Good Problem

Purpose Organizational learning relies on problem-solving as a way to generate new knowledge. Good problem solvers should adopt a problem-solving orientation (PSO) that analyzes the causes of problems to arrive at an effective solution. The purpose of this paper is to investigate this relevant, though underexplored, topic by examining two important antecedents of PSO: knowledge sharing mechanisms and transformational leaders’ support. Design/methodology/approach Hierarchical linear modeling analyses were performed on a sample of 131 workers in 12 plants. A questionnaire was designed to collect data from shop-floor employees. Knowledge sharing was measured using the mechanisms of participative practices and standardized practices. Management support was assessed based on the extent to which supervisors engaged in transformational leadership. Findings Knowledge sharing mechanisms are an antecedent of PSO behavior, but management support measured in terms of transformational leadership is not. However, transformational leadership affects the use of knowledge sharing mechanisms that, in turn, is positively related to PSO behavior. Practical implications The research provides practical guidance for practitioners to understand how to manage knowledge in the workplace to promote employees’ PSO behaviors. Originality/value Though problem-solving activities are intrinsic in any working context, PSO is still very much underrepresented and scarcely understood in knowledge management studies. This study fills this gap by investigating the antecedents of PSO behavior.

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