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The purpose of this paper is to explore the relationship between multivariate process capability indices and loss functions for both nominal‐the‐best and smaller‐the‐better cases, so the likelihood and consequences resulting from the nonconforming of a manufacturing process or an environmental system can be evaluated simultaneously.

In this paper, the authors present a new approach of correlated risk assessment by linking the multiple process capability indices and loss functions, in which the multivariate process capability indices and multivariate loss functions describe the likelihood and consequences as a result of nonconformities in multivariate manufacturing or environmental system, respectively. Then, the associated relationship equations are developed using multivariate methods. Moreover, a step‐by‐step procedure is provided to facilitate the implementation of the correlated risk assessment.

Given the multivariate process capability indices, the authors show that the expected loss can be estimated by developed relationship equations and two numerical examples are also given, to demonstrate how the correlated manufacturing and environmental risks can be properly assessed by linking the multivariate process capability indices and multivariate loss function.

The risk information of likelihood and expected loss, classified in the four planning zones of a strategic planning matrix, provides practising managers and engineers with a decision‐making tool for prioritizing their quality improvement projects when conducting risk assessment for any multivariate process or environmental system.

Once the existing quality/environmental problems and their Key Performance Indicators are identified, one may conduct risk assessment by applying the relationship equations to evaluate the impact of correlated risk on manufacturing processes or multiple environmental emissions inside company; this can lead to the direction of continuous improvement for any industry.

The purpose of this paper is to study the interaction of economics of production with process quality, when multiple key quality characteristics are present. Specifically, the paper aims to analyse the possibility of investing in a production process to reduce its variances and the impact on a multivariate quality loss function.

A bivariate inventory‐planning model is developed, in which the optimal investment for reducing process variances and the optimal lot size are jointly determined. A case study with industrial data is presented to illustrate the possible solution procedures and the potential advantages of the proposed model.

It is found that by using the previous approaches to analyse the interaction between the economics of production and process quality, a company will underestimate the cost of quality, especially the expected external failure cost (quality loss), and ultimately invest less into the prevention activities to improve the process.

The proposed model can help managers to compare different production processes and also guide the managers towards better choices for process improvement. To the best of our knowledge, this paper is the first to integrate the economic production quantity (EPQ) problem with the process quality consideration for products with multiple quality characteristics.

The selection of the optimal process target for a manufacturing process is critically important as it directly affects the defect rate, rejection and rework costs, and the loss to customers. A recent review of process target literature suggests that future work should incorporate models using multiple quality characteristics. Thus, the purpose of this paper is to create a more flexible and realistic approach to solving the multi‐response process target problem.

A design of experiments methodology is proposed to provide estimates of process parameters and a nonlinear constrained optimization scheme is employed to identify optimal settings.

The approximation of cost savings undoubtedly has a higher degree of accuracy than in the case where the engineer assumes values for the process parameters. Furthermore, greater flexibility is obtained in finding solutions that support both the manufacturer and the customer.

This methodology relies on controlled experimentation and the replication of observations made on multiple nominal‐the‐best quality characteristics. Future research may include examining the effects of using smaller‐the‐better or larger‐the‐better type characteristics.

Unlike traditional attempts at solving the process target problem where the process mean, variance, and covariance between characteristics are assumed known in advance, this paper uses an approach that removes these assumptions, thereby providing a more practical depiction of the overall system. Furthermore, this methodology broadens the scope of process target problem research by seeking the simultaneous optimization of process parameters and considering a loss in quality attributed to deviation from a target value.

Based on univariate dimension-reduction model, this study aims to propose an adaptive anisotropic response surface method (ARSM) and its high-order revision (HARSM) to balance the accuracy and efficiency for response surface method (RSM).

First, judgment criteria for the constitution of a univariate function are derived mathematically, together with the practical implementation. Second, by combining separate polynomial approximation of each component function of univariate dimension-reduction model with its constitution analysis, the anisotropic ARSM is proposed. Third, the high-order revision for component functions is introduced to improve the accuracy of ARSM, namely, HARSM, in which the revision is also anisotropic. Finally, several examples are investigated to verify the accuracy, efficiency and convergence of the proposed methods, and the influence of parameters on the proposed methods is also performed.

The criteria for constitution analysis are appropriate and practical. Obtaining the undetermined coefficients for every component functions is easier than the existing RSMs. The existence of special component functions is useful to improve the efficiency of the ARSM. HARSM is helpful for improving accuracy significantly and it is more robust than ARSM and the existing quadratic polynomial RSMs and linear RSM. ARSM and HARSM can achieve appropriate balance between precision and efficiency.

The constitution of univariate function can be determined adaptively and the nonlinearity of different variables in the response surface can be treated in an anisotropic way.

The purpose of this paper is to propose a new method of robust parameter design for dynamic multi‐response system. The objectives are to resolve the correlations among multiple responses and the uncertainty of system with incomplete information.

First, desirability function is used to measure dynamic system sensitivity and system variation, and principal component analyses on the two indices are conducted. Second, the grey relational grade (GRD) between principal component sequences of the two indices and their respective ideal sequences, gained by grey relational analysis, is converted to an integrated GRD (IGRD) index by means of TOPSIS method, and then the optimal level combination of controllable factors is identified based on the IGRD index.

It was found that the optimal factor level combination obtained by the proposed method is nearest the ideal solution and farthest from the negative ideal solution. The validity and superiority of the proposed method are confirmed through two illustrative examples.

It should be noted that the proposed method fails to consider the interaction effects between controllable factors and noise factors.

The method proposed in the paper effectively integrates several common methods to optimize a dynamic multiple responses system based on Taguchi's robust parameter design. These methods do not involve complicated mathematical theory, and are therefore easy for practitioners to use in engineering practice.

The purpose of this paper is to find out a scientific method to evaluate quality of complex products, whose quality is different from general products.

Based on interval grey number theory, reliability analysis method and stochastic network theory, authors have established grey success tree analysis-graph evaluation and review technique (GSTA-GERT) model in this paper.

Comparing complex products and general products, authors have found that complex products have two characters, i.e. quality of manufacture and quality of service. Furthermore, this paper has proved the GSTA-GERT model is a scientific and reasonable approach to estimate quality of complex products from the sight of manufacture-service network.

This paper has established GSTA-GERT model, which surmounts the defect of traditional estimation method, such as lacking logic analysis in the method of analytic hierarchy process.

The purpose of this paper is to develop a double-objective economic statistical design (ESD) of (X ‾) control chart under Weibull failure properties with the Linex asymmetric loss function. The authors have expressed the probability of type II error (β) as the statistical objective and the expected cost as the economic objective.

The design used in this study is based on a double-objective economic statistical design of (X ‾) control chart with Weibull shock model via applying Banerjee and Rahim's model for non-uniform and uniform schemes with Linex asymmetric loss function. The results in the least average cost and β in uniform and non-uniform schemes by Linex loss function, compared with the same schemes without loss function.

Numerical results indicate that it is not possible to reduce the second type of error and costs at the same time, which means that by reducing the second type of error, the cost increases, and by reducing the cost, the second type of error increases, both of which are very important. Obtained based on the needs of the industry and which one has more priority has the right to choose. These designs define a Pareto optimal front of solutions that increase the flexibility and adaptability of the X ‾ control chart in practice. When the authors use non-uniform schemes instead of uniform schemes, the average cost per unit time decreases by an average and when the authors apply loss function, the average cost per unit time increases by an average. Also, this quantity for double-objective schemes with loss function compared to without loss function schemes in cases uniform and non-uniform increases. The reason for this result is that the model underestimated the costs before using the loss function.

This research adds to the body of knowledge related to flexibility in process quality control. This article may be of interest to quality systems experts in factories where the choice between cost reduction and statistical factor reduction can affect the production process.

The cost functions for double-objective uniform and non-uniform sampling schemes with the Weibull shock model based on the Linex loss function are presented for the first time.

Many studies only take into account the ply stacking sequence as the design variable to determine the optimal ply drop-off location; however, it is necessary to optimize other parameters that have a direct influence on the ply drop-off site such as which plies should be dropped and in which longitudinal direction. That way, the purpose of this study is to find the most significant design variables relative to the drop-off location considering the transversal and longitudinal positions, seeking to achieve the optimal combination of ply drop-off locations that provides excellent performance for the laminate plate.

This study aims to determine the optimal drop-off location in a laminate plate using the finite element method and an approach statistical with design of experiments (DOE).

The optimization strategy using DOE revealed to be satisfactory for analyzing laminate structures with ply drop-offs, demonstrating that not all design factors influence the response variability. The failure criterion response variable revealed a poor fit, with an adjusted coefficient of determination lower than 60%, thus demonstrating that the response did not vary with the ply drop-off location. Already the strain and natural frequency response variables presented high significance. Finally, the optimization strategy revealed that the optimal drop-off location that minimizes the strain and maximizes the natural frequency is the ply drop-off located of the end plate.

It was also noted that many researchers prefer evolutionary algorithms for optimizing composite structures with ply drop-offs, being scarce to the literature studies involving optimization strategies using response surface methodology. In addition, many studies only take into account the ply stacking sequence as the design variable to determine the optimal ply drop-off location; however, in this study, the authors investigated other important parameters that have direct influence on the ply drop-off site such as which plies should be dropped and in which longitudinal direction.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.