341 research outputs found

    Multiresponse Optimization Methodology Considering Related Quality Characteristics

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    Engineering problems often involve many conflicting quality characteristics that must be optimized simultaneously. Engineers are required to select suitable design parameter values which provide better trade-off among all quality characteristics. Multiresponse optimization is one of the most essential tools for solving engineering problems involving multiple quality characteristics. Optimizing several quality characteristics when the quality characteristics are correlated makes the optimization process more complex. The aim of this research is to evaluate the performance of several existing multiresponse optimization methods and investigate their capabilities in dealing with correlated quality characteristics. This study also investigates the impact of uncertainty in terms of input parameter selection. A new multi-response optimization approach has been proposed for solving correlated quality characteristics. The proposed approach is compared with the existing methods and found more robust in terms dealing with uncertainty in target selection. The comparative study and application of the proposed approach is demonstrated by considering two examples from the literature having correlated quality characteristics

    An Integrated Probability-Based Approach for Multiple Response Surface Optimization

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    Nearly all real life systems have multiple quality characteristics where individual modeling and optimization approaches can not provide a balanced compromising solution. Since performance, cost, schedule, and consistency remain the basics of any design process, design configurations are expected to meet several conflicting requirements at the same time. Correlation between responses and model parameter uncertainty demands extra scrutiny and prevents practitioners from studying responses in isolation. Like any other multi-objective problem, multi-response optimization problem requires trade-offs and compromises, which in turn makes the available algorithms difficult to generalize for all design problems. Although multiple modeling and optimization approaches have been highly utilized in different industries, and several software applications are available, there is no perfect solution to date and this is likely to remain so in the future. Therefore, problem specific structure, diversity, and the complexity of the available approaches require careful consideration by the quality engineers in their applications

    Robust Parameter Design of Functional Responses Based on Bayesian SUR Models

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    As for the robust parameter design of functional responses, a Bayesian Seemingly Unrelated Regression (SUR) model is proposed to take into account the model uncertainty and response variability in this paper. First of all, the SUR model is used to build the functional relationship between the output responses and the input factors at different time points. Also, Bayesian analysis of the SUR model is performed to consider the influence of the model parameter uncertainty on the research results. Secondly, the process means and variances of the functional responses at different time points are estimated by the posterior samples of the simulated responses. Moreover, an integrated performance index (i.e. mean square error) is establish by using the above process means and variances. Then, the optimal parameter settings may be found by minimizing the MSE performance index. Finally, the advantages of the proposed method are illustrated by an example from the literature

    Cohort aggregation modelling for complex forest stands: Spruce-aspen mixtures in British Columbia

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    Mixed-species growth models are needed as a synthesis of ecological knowledge and for guiding forest management. Individual-tree models have been commonly used, but the difficulties of reliably scaling from the individual to the stand level are often underestimated. Emergent properties and statistical issues limit their effectiveness. A more holistic modelling of aggregates at the whole stand level is a potentially attractive alternative. This work explores methodology for developing biologically consistent dynamic mixture models where the state is described by aggregate stand-level variables for species or age/size cohorts. The methods are demonstrated and tested with a two-cohort model for spruce-aspen mixtures named SAM. The models combine single-species submodels and submodels for resource partitioning among the cohorts. The partitioning allows for differences in competitive strength among species and size classes, and for complementarity effects. Height growth reduction in suppressed cohorts is also modelled. SAM fits well the available data, and exhibits behaviors consistent with current ecological knowledge. The general framework can be applied to any number of cohorts, and should be useful as a basis for modelling other mixed-species or uneven-aged stands.Comment: Accepted manuscript, to appear in Ecological Modellin

    Bi-Objective Optimization Problems—A Game Theory Perspective to Improve Process and Product

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    Publisher Copyright: © 2022 by the authors. This research received no external funding.Cost-effective manufacturing processes or products are no longer the only requirements for business sustainability. An approach based on Game Theory is suggested to find solutions for bi-objective problems. In particular, Stackelberg’s technique is employed and complemented with the Factors Scaling tool to help the users in defining its strategy for optimizing process and product quality characteristics. No subjective information (shape factors, weights, and/or any other preference information) is required from the users, and basic computational background is enough for implementing it. Two case studies provide evidence that the suggested easy-to-use approach can yield nondominated solutions from a small number of Leader–Follower cycles, what reinforces its usefulness for bi-objective optimization problems.publishersversionpublishe

    Modelling the kinetics of osmotic dehydration of mango: Optimizing process conditions and pre-treatment for health aspects

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    The kinetics of mass transfer and vitamin C loss in mango during osmotic dehydration (OD) were described by mathematical models. Water loss (WL) and weight reduction (WR) was modelled by Weibull's model, soluble solid gain (SSG) was better described by Peleg's model. Vitamin C loss was described by a multiresponse model incorporating both degradation and leaching processes into the OD-solution. Effects of vacuum impregnation (VI) and pectin methylesterase (PME) addition on the model parameters were evaluated. VI increases SSG indicated by a 55% lower value of k2 in the Peleg model (P < 0.05). PME addition showed no significant effect on the mass transfer kinetics. The major mechanism of vitamin C loss during OD was degradation. The pretreatments have no significant effect on degradation and leaching rate constants of vitamin C. The combination of modelling the mass transfer and vitamin C retention was shown to be valuable in optimizing the OD process design to enhance the health-promoting value of OD mango (sugar content, vitamin C) and processing time.</p

    Development of the D-Optimality-Based Coordinate-Exchange Algorithm for an Irregular Design Space and the Mixed-Integer Nonlinear Robust Parameter Design Optimization

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    Robust parameter design (RPD), originally conceptualized by Taguchi, is an effective statistical design method for continuous quality improvement by incorporating product quality into the design of processes. The primary goal of RPD is to identify optimal input variable level settings with minimum process bias and variation. Because of its practicality in reducing inherent uncertainties associated with system performance across key product and process dimensions, the widespread application of RPD techniques to many engineering and science fields has resulted in significant improvements in product quality and process enhancement. There is little disagreement among researchers about Taguchi\u27s basic philosophy. In response to apparent mathematical flaws surrounding his original version of RPD, researchers have closely examined alternative approaches by incorporating well-established statistical methods, particularly the response surface methodology (RSM), while accepting the main philosophy of his RPD concepts. This particular RSM-based RPD method predominantly employs the central composite design technique with the assumption that input variables are quantitative on a continuous scale. There is a large number of practical situations in which a combination of input variables is of real-valued quantitative variables on a continuous scale and qualitative variables such as integer- and binary-valued variables. Despite the practicality of such cases in real-world engineering problems, there has been little research attempt, if any, perhaps due to mathematical hurdles in terms of inconsistencies between a design space in the experimental phase and a solution space in the optimization phase. For instance, the design space associated with the central composite design, which is perhaps known as the most effective response surface design for a second-order prediction model, is typically a bounded convex feasible set involving real numbers due to its inherent real-valued axial design points; however, its solution space may consist of integer and real values. Along the lines, this dissertation proposes RPD optimization models under three different scenarios. Given integer-valued constraints, this dissertation discusses why the Box-Behnken design is preferred over the central composite design and other three-level designs, while maintaining constant or nearly constant prediction variance, called the design rotatability, associated with a second-order model. Box-Behnken design embedded mixed integer nonlinear programming models are then proposed. As a solution method, the Karush-Kuhn-Tucker conditions are developed and the sequential quadratic integer programming technique is also used. Further, given binary-valued constraints, this dissertation investigates why neither the central composite design nor the Box-Behnken design is effective. To remedy this potential problem, several 0-1 mixed integer nonlinear programming models are proposed by laying out the foundation of a three-level factorial design with pseudo center points. For these particular models, we use standard optimization methods such as the branch-and-bound technique, the outer approximation method, and the hybrid nonlinear based branch-and-cut algorithm. Finally, there exist some special situations during the experimental phase where the situation may call for reducing the number of experimental runs or using a reduced regression model in fitting the data. Furthermore, there are special situations where the experimental design space is constrained, and therefore optimal design points should be generated. In these particular situations, traditional experimental designs may not be appropriate. D-optimal experimental designs are investigated and incorporated into nonlinear programming models, as the design region is typically irregular which may end up being a convex problem. It is believed that the research work contained in this dissertation is the initial examination in the related literature and makes a considerable contribution to an existing body of knowledge by filling research gaps

    Development of a Rapid UHPLC-PDA Method for the Simultaneous Quantification of Flavonol Contents in Onions (Allium cepa L.)

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    Onion, one of the most consumed vegetables in the world, is also known to contain high levels of antioxidant compounds, with protective effects against different degenerative pathologies. Specifically, onion is rich in flavonols, mainly quercetin derivatives, which are compounds with high antioxidant and free radical scavenging power. For this reason, it is of the utmost importance to count on optimal analytical methods that allow for the determination and quantification of these compounds of interest. A rapid ultra-high performance liquid chromatography (UHPLC)-photo-diode array (PDA) method for the separation of the major flavonols in onions was developed using a Box-Behnken design in conjunction with multiresponse optimization on the basis of the desirability function. The conditions that provided a successful separation were 9.9% and 53.2% of phase B at the beginning and at the end of the gradient, respectively; 55 degrees C column working temperature; and 0.6 mL min(-1) flow rate. The complete separation was achieved in less than 2.7 min with excellent chromatographic characteristics. The method was validated, and its high precision, low detection and quantification limits, good linearity, and robustness were confirmed. The correct applicability of the method improves the analysis of the raw material, increasing the quality of onions and its subproducts in terms of bioactive compounds and functional characteristics for consumers

    A Robust Multi Response Surface Approach for Optimization of Multistage Processes

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    Purpose: In a multistage process, the final quality in the last stage not only depends on the quality of the task performed in that stage but also is 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/approach: In 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. Findings: The 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 global criterion (GC) index in optimization phase, will perform better than the classical full information maximum likelihood (FIML) estimation method. Originality/value: To 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
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