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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 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.

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.

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 GC index in optimization phase, will perform better than the classical full information maximum likelihood (FIML) estimation method.

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.

This study aims to gain new insight into how a set of maqasid-based consumption intelligence variables mediates exogenous variables (i.e. religiosity, Islamic university role and normative belief) and halal purchase intention as an endogenous variable.

The research model is empirically tested with a data set of 370 responses retrieved from the students of the millennial generation from the cross Islamic state university in Indonesia. Data were analysed with Partial Least Square Structural Equation Modelling (PLS-SEM). The sample size of this study is computed with preliminary power analysis.

The SEM finding revealed that two maqasid-based consumption intelligence variables had mediated the exogenous and endogenous variables, i.e. halal purchase intention. These variables mediating the exogenous and endogenous variables have explained 63.5 R² variances in halal purchase intention. Concerning individual impact size of cognition and motivation as a component of maqasid-based consumption intelligence has shown medium-level effect size (f²) in mediating the halal purchase intention. Interestingly, the exogenous variable does not directly affect halal purchase intention but must be mediated with maqasid-based consumption intelligence variables. However, before including variables of maqasid-based consumption intelligence, the R² was relatively small. It is just 30.4% in R² that explains the variance of halal purchase intention.

This study explores maqasid-based consumption intelligence as a relatively new model to explain the variable halal purchase intention. Therefore, it takes many types of exogenous variables to test how relevant maqasid-based consumption intelligence variables can define endogenous variables. Notwithstanding, this study does not do that because it only limits three exogenous variables (i.e. religiosity, Islamic university role and normative belief). Therefore, in further research, these limitations seem to be perfected by other scholars concerned about halal purchase intention.

The findings of this study allow Islamic universities to mainstream halal issues as a subject of learning, especially concerning consumption ethics. In addition, the empirical results of this study encourage the industry to pay attention to the essential components in determining the halal quality of the products offered because the millennial generation is now very aware of the importance of halal products.

This study contributes to the existing literature on halal purchase intention by testing maqasid-based consumption intelligence variables as mediators. Moreover, this is a pioneer study to consider and construct maqasid-based consumption intelligence as a model that explains halal purchase intention.

This study examines five national public policy areas where states and local governments received grants-in-aid from the federal government; these grants approximate a fifth of their yearly revenue budgets. Knowing the historical trends and concentrations can minimize expectation errors of practitioners and policy makers and facilitate future revenue planning. The grants examined between 1940 and 2010 include income security, health, education and training, economic and regional development, and transportation. The study uses agency theory to rationalize relationships among the governments, and applies statistical modeling, multiple means comparisons and discriminant analyses to test whether there are distinct policy concentrations and differences among policy regimes. Our findings show transfers were continuous, physically important and unaffected significantly by adjustments due to size and prices. The study found concentrations and differences among policy regimes.

This study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T² chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.

Multivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T² statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.

The finding of this paper is that the points and lines in the classic Hotelling T² chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T² control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.

Modifying the classical Hotelling T² control chart by integrating with the concept of graph theory and neural network is new of its kind.

This paper aims to propose a combined methodology to help decision makers in evaluating and selecting the most effective part feeding system.

As a first step of the methodology, a hierarchical clustering analysis is applied to design a kitting or hybrid feeding system. Second, activity-based costing methodology is applied to determine which system is better according to their costs. Besides, sensitivity analysis is implemented to observe the behavior of the system in case of the takt time changes.

Using kitting systems purely can lead to problems because of the big and expensive parts in the mixed-model assembly systems. Therefore, the hybrid feeding policy can provide better solutions for such systems.

A case study is conducted in a company and the most produced product of the company is considered to design the part feeding system. Results indicated that transportation cost has a large proportion on the total cost and the hybrid feeding policy may be a good solution to reduce this cost.

The paper includes implications for the design of hybrid feeding systems in lean-based assembly lines. The proposed methodology may be a practical tool for decision makers to design and decide on the part feeding policy.

Kitting design has not been studied yet to the best of the authors’ knowledge. Besides, there is no certain decision methodology indicating which system is better. In this study, different methods are combined as a new methodology with the purpose of industrial decision-making.

This paper aims to offer a quantitative methodology to identify and measure the gap between the communicated brand identity and perceived brand image by channel members and the consumers. Brand marketers communicate with their target consumers to make them aware of brand identity and communicate the same way to the channel members directly. Channel members, in turn, convey the same to the end-users. Thus, a proper alignment of these three crucial nodes, namely, brand marketers, channel members and consumers, is inevitable for the efficient transfer of brand identity. However, in reality, not all are successful to synchronize communicated brand identity and image perception. So, the identification and measurement of identity-image gap is essential.

Based on the literature review, the authors propose a conceptual model for the study and generate the basic research questions. In this study, Kapferer’s brand identity prism has been taken as the focal point of study to measure brand identity. So far as the vector measure is concerned, a p-dimensional setup is present, each dimension representing each facet of Kapferer’s brand identity prism. Now, given these sets of observations, the authors introduce for each set, a multivariate distributional setup to represent the underlying population behavior.

In this study, a theoretical framework is proposed to identify and measure brand identity and image consistency. To minimize the problem associated with subjective decisions, an objective procedure has been proposed to measure the brand knowledge structure of company personnel, consumers and channel members about the considered brands. The results of this study show that brand knowledge consistency is missing among marketers, consumers and channel members for considered brands. The proposed methodology may help marketers to measure the identity-image gap in a more objective manner with pinpoint accuracy by adopting a quantitative approach.

The proposed methodology may help marketers to measure the identity-image gap in a more objective manner with pinpoint accuracy by adopting a quantitative approach. Once a gap is identified, it will be easy for marketers to adopt possible measures to bridge the gap. This helps brand marketers to understand the branding process more objectively.

To the best of the authors' knowledge, there is a lack of concrete quantitative approach, attempting to discuss the methodology to measure the gap between brand identity facets and brand image. In this backdrop, this might be the first paper offering a quantitative methodology to identify and measure the gap between the communicated brand identity and perceived brand image by channel members and the consumers.

The purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.

The algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.

The first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.

Data points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.

First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.

The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.

A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

The purpose of this paper is to appraise methodological rigor in the application of discriminant analysis (DA) in export-focused research and to offer guidelines for future studies.

The sample includes 89 empirical peer-reviewed studies, comprising 102 models published over the period 1979-2014. Content analysis and vote counting are used to evaluate each of these studies.

This review highlights major flaws in the application of DA in export research. The shortcomings are self-evident particularly concerning suitability of DA for research context, completeness in the reporting of descriptive results, and validity and reliability of predictive results.

The study takes the position that the lack of methodological rigor may be undermining the eminence of knowledge in exporting, and this has extensive implications for both researchers and practitioners.

This review outlines steps to assess methodological rigor associated with DA and offers guidelines for scholars seeking to enhance rigor in future research.

This paper is to propose a novel generalized grey target decision method (GGTDM) with index and weight both containing mixed types of data.

The decision-making steps of the proposed approach are as follows. First, all mixed attribute values of alternatives and weights are transformed into binary connection numbers and also comprised two-tuple (determinacy, uncertainty) numbers. Then, the two-tuple (determinacy, uncertainty) numbers of target center indices are calculated. Next, the certain weights are determined by the Gini–Simpson (G–S) index-based method. Following this, the comprehensive-weighted Kullback–Leibler distances (CWKLDs) of all alternatives and the target center are obtained. Finally, the alternative ranking relies on the CWKLD considering the smaller value as the better option.

The certain weights determined by the improved Gini–Simpson index (IGSI) based method are more accurate in compared with that by the proximity-based method and the weight function method. The discrimination ability of alternatives ranking of the proposed approach is stronger than that of the compared comprehensive-weighted proximity (CWP) based method and comprehensive-weighted Gini–Simpson index (CWGSI) based method.

The proposed method fulfills the decision-making task relying on CWKLD, which solves the uncertain measurement from the viewpoint of entropy.

The proposed approach adopts the IGSI to transform uncertain weights into certain ones and takes the CWKLD as the basis for the decision-making.