Applying fuzzy quality function deployment (QFD) to identify solutions of service delivery system for port of Kaohsiung

The main purpose of this paper is to apply fuzzy quality function deployment (QFD) model to identify solutions of service delivery system (SDS) for port of Kaohsiung from the viewpoints of customers. At first, to facilitate the main issue of the QFD problem, however, the ‘what’ question of customer needs and ‘how’ problem of the services have to be made, which are two major components and be emphasized on the house of quality (HOQ) matrices. In conjunction with fuzzy sets theory, hence, the systematic procedures using fuzzy QFD were proposed in this paper. Subsequently, a case study for port of Kaohsiung demonstrated the systematic appraisal process for identifying solutions of SDS. The results of empirical study show that (1) 10 key factors are deemed as to have priority to improve the quality of SDS for Kaohsiung port; and (2) eight feasible solutions for improving service quality performance are identified. Moreover, it is suggested that port Authority of Kaohsiung should listen attentively the voice of customers and emphasize on exploiting these customer requirements effectively. And then develop the ‘how’ issues of profiles of solutions, which should continuously strengthen the perspectives of customer, internal business process, and learning and growth, respectively.     

Beta spatial linear mixed model with variable dispersion using Monte Carlo maximum likelihood

We propose a beta spatial linear mixed model with variable dispersion using Monte Carlo maximum likelihood. The proposed method is useful for those situations where the response variable is a rate or a proportion. An approach to the spatial generalized linear mixed models using the Box–Cox transformation in the precision model is presented. Thus, the parameter optimization process is developed for both the spatial mean model and the spatial variable dispersion model. All the parameters are estimated using Markov chain Monte Carlo maximum likelihood. Statistical inference over the parameters is performed using approximations obtained from the asymptotic normality of the maximum likelihood estimator. Diagnosis and prediction of a new observation are also developed. The method is illustrated with the analysis of one simulated case and two studies: clay and magnesium contents. In the clay study, 147 soil profile observations were taken from the research area of the Tropenbos Cameroon Programme, with explanatory variables: elevation in metres above sea level, agro‐ecological zone, reference soil group and land cover type. In the magnesium content, the soil samples were taken from 0‐ to 20‐cm‐depth layer at each of the 178 locations, and the response variable is related to the spatial locations, altitude and sub‐region.     

A multiple linear regression model for imprecise information

In standard regression analysis the relationship between the (response) variable and a set of (explanatory) variables is investigated. In the classical framework the response is affected by probabilistic uncertainty (randomness) and, thus, treated as a random variable. However, the data can also be subjected to other kinds of uncertainty such as imprecision. A possible way to manage all of these uncertainties is represented by the concept of fuzzy random variable (FRV). The most common class of FRVs is the LR family (LR FRV), which allows us to express every FRV in terms of three random variables, namely, the center, the left spread and the right spread. In this work, limiting our attention to the LR FRV class, we consider the linear regression problem in the presence of one or more imprecise random elements. The procedure for estimating the model parameters and the determination coefficient are discussed and the hypothesis testing problem is addressed following a bootstrap approach. Furthermore, in order to illustrate how the proposed model works in practice, the results of a real-life example are given together with a comparison with those obtained by applying classical regression analysis.     

Fuzzy Control Chart A Better Alternative for Shewhart Average Chart

This paper through a real illustrative example and a power test shows that designing a fuzzy control chart for process average of a continuous (variable) quality characteristic with a warning line is a better alternative to Shewhart chart in many respects, like providing better neural view to inspectors, offering different strategic options for company to choose, detecting the desire shifts more quickly, and more sensibility to small shifts without any complexity augmentation to the chart.     

Measuring process capability index <Emphasis Type="Italic">C</Emphasis><Subscript><Emphasis Type="Italic">pm</Emphasis></Subscript> with fuzzy data

The process capability index C _pm , which considers the process variance and departure of the process mean from the target value, is important in the manufacturing industry to measure process potential and performance. This paper extends its applications to calculate the process capability index [(C)\tilde]_pm{\tilde {C}_{pm} } of fuzzy numbers. In this paper, the α-cuts of fuzzy observations are first derived based on various values of α. The membership function of fuzzy process capability index [(C)\tilde]_pm{\tilde {C}_{pm} } is then constructed based on the α-cuts of fuzzy observations. An example is presented to demonstrate how the fuzzy process capability index [(C)\tilde]_pm{\tilde {C}_{pm} } is interpreted. When the quality characteristic cannot be precisely determined, the proposed method provides the most possible value and spread of fuzzy process capability index [(C)\tilde]_pm{\tilde {C}_{pm} }. With crisp data, the proposed method reduces to the classical method of process capability index C _pm .     

Estimation of the most influential science and technology factors for economic growth forecasting by soft computing technique

Economic growth may be developed on the basis on combination of different factors. In this investigation was analyzed the economic growth forecasting based on the different factors. The main goal was to analyze the influence of science and technology factors on the economic growth. Gross domestic product (GDP) was used as economic growth indicator. The method of adaptive neuro fuzzy inference system (ANFIS) was applied to the data in order to select the most influential factors for the GDP growth rate forecasting. Ten inputs are considered: research and development (R&D) expenditure in GDP, scientific and technical journal articles, patent applications for nonresidents, patent applications for residents, trademark applications for nonresidents, trademark applications for residents, total trademark applications, researchers in R&D, technicians in R&D and high-technology exports. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of GDP growth rate.     

A fuzzy service quality based approach for performance evaluation of educational units

Educational unites try to make efficient use of resources to achieve higher service qualities. This paper presents a new hybrid fuzzy approach based on service quality for performance evaluation of educational units. The proposed approach includes four stages: firstly, evaluation and gap analysis of students' expectations and perceptions in the service quality of educational units are performed based on fuzzy SERVQUAL questionnaires. In the second stage, the corresponding weights of dimensions and sub-dimensions of SERVQUAL are determined via fuzzy AHP method. In the third stage, the educational units are ranked using fuzzy TOPSIS method with respect to service quality sub-dimensions. Finally, a fuzzy DEA method is applied to determine the efficiency of educational units based on their service quality ratings. The approach is used for performance evaluation of eight higher education institutes in Iran. The proposed approach increased the accuracy of performance evaluation of educational units based on service quality.     

Hotelling’s <Emphasis Type="Italic">T</Emphasis><Superscript>2</Superscript> control chart with two adaptive sample sizes

Some quality control schemes have been developed when several related quality characteristics are to be monitored. The familiar multivariate process monitoring and control procedure is the Hotelling’s T ² control chart for monitoring the mean vector of the process. It is a direct analog of the univariate shewhart [`(x)]{\bar{x}} chart. As in the case of univariate, the ARL improvements are very important particularly for small process shifts. In this paper, we study the T ² control chart with two-state adaptive sample size, when the shift in the process mean does not occur at the beginning but at some random time in the future. Further, the occurrence time of the shift is assumed to be exponentially distributed random variable.     

Determination of the most influential factors for number of patents prediction by adaptive neuro-fuzzy technique

Number of patents may be developed on the basis on different natural and science and technological factors. Number of patents prediction based on the different factors in many countries is analyzed in this investigation. These factors represent natural and science resources. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for the number of patents prediction. Five inputs are considered: research and development (R&D) resources, natural resources, quality of academic institutions, quality of collaboration with the private sector and quality of education. As the ANFIS output, number of patents is considered. The ANFIS process for variable selection is also implemented in order to detect the predominant factors affecting the prediction of number of patents. Results show that the R&D is the most influential factor for the number of patents prediction.     

Using Complex Surveys to Estimate the L1‐Median of a Functional Variable: Application to Electricity Load Curves

Mean profiles are widely used as indicators of the electricity consumption habits of customers. Currently, in Électricité De France (EDF), class load profiles are estimated using point‐wise mean profiles. Unfortunately, it is well known that the mean is highly sensitive to the presence of outliers, such as one or more consumers with unusually high‐levels of consumption. In this paper, we propose an alternative to the mean profile: the L ₁ ‐ median profile which is more robust. When dealing with large data sets of functional data (load curves for example), survey sampling approaches are useful for estimating the median profile avoiding storing the whole data. We propose here several sampling strategies and estimators to estimate the median trajectory. A comparison between them is illustrated by means of a test population. We develop a stratification based on the linearized variable which substantially improves the accuracy of the estimator compared to simple random sampling without replacement. We suggest also an improved estimator that takes into account auxiliary information. Some potential areas for future research are also highlighted.     

Neyman–Pearson Lemma for Fuzzy Hypotheses Testing with Vague Data

In hypotheses testing, such as other statistical problems, we may confront imprecise concepts. One case is a situation in which both hypotheses and observations are imprecise. This paper tries to develop a new approach for testing fuzzy hypothesis when the available data are fuzzy, too. First, some definitions are provided, such as: fuzzy sample space, fuzzy-valued random sample, and fuzzy-valued random variable. Then, the problem of fuzzy hypothesis testing with vague data is formulated. Finally, we state and prove a generalized Neyman–Pearson Lemma for such problem. The proposed approach is illustrated by some numerical examples.     

Applying fuzzy quality function deployment to identify service management requirements for customer quality needs

In this paper a fuzzy quality function deployment to identify service management requirements for customer quality needs is developed. First, customer quality needs with importance degree have been investigated followed by development of service management requirements for satisfying the observed customer quality needs. Furthermore, construct a fuzzy relation matrix to link service management requirements and customer quality needs based on cross-functional expertise. The aggregated fuzzy assessments of various service management requirements are ranked to determine their importance priority. Finally, an empirical study for identifying the service management requirements for customer quality is used to demonstrate the proposed approach.     

Causality in social theory via linguistic fuzzy logic

I present a new approach to the study of causality in social theory using linguistic fuzzy logic as a framework. This approach differs from conventional analysis of causality on two fronts. First, all variables are considered to possess two degrees of freedom (or variation): a linguistic nuance value, which corresponds to what we conventionally refer to as interval or categorical value, and a linguistic truth value, which measures our confidence level in this nuance value. Second, combining this double fuzzification of variables with linguistic fuzzy logic I propose new tools for studying fuzzy causality. The linguistic fuzzy logic approach is illustrated through a re-examination of Skocpol’s (1979, States and social revolutions: a comparative analysis of France, Russia, and China. Cambridge University Press, Cambridge) theory of social revolution.     

A quality approach to airport management

This paper discusses aspects of airport service quality evaluation using fuzzy multicriteria analysis and the alpha-cut concept. It presents a fuzzy multicriteria methodology for analysing a complex set of quality variables. This methodology develops a step by step set of indicators that allow managers have a holistic view of the quality dimension. It also helps the understanding of cause and effect relation, it helps tracking service quality problems and benchmarking them. From the alpha-cut concept, by defining upper and lower limits, this approach seeks to assist managers in evaluating a complex number of quality criteria and decision-makers’ behaviour. This approach gives managers a broader view of comparative perceptions of quality among the airports in a given sample, by presenting the results of the analysis in fuzzy form and allowing managers to consider different styles of evaluation. The methodology discussed is applied to six Brazilian international airports.     

Analysing Your Multivariate Data as a Pictorial: A Case for Applying Biplot Methodology?

This paper illustrates the versatility of biplot methodology when analysing multivariate data from diverse disciplines. The modern approach of Gower & Hand (1996) whereby biplots are regarded as multivariate analogues of ordinary scatter plots is utilised for extending biplot methodology introducing several novel applications. Focus is on biplot applications where the merits of principal component biplots and canonical variate analysis biplots are illustrated with data sets from higher education, the manufacturing industry, the mining industry, agriculture, finance and archaeology. It is shown how to equip biplots with quality regions, classification regions and acceptance regions; how a -bags superimposed on biplots provide a quantification of the multidimensional overlap of classes as well as enable biplots to be used with large data sets; how to use biplots for exploring multi-dimensional reality and in sophisticated classification procedures.     

An efficient Bayesian approach to multiple structural change in multivariate time series

This paper provides a feasible approach to estimation and forecasting of multiple structural breaks for vector autoregressions and other multivariate models. Owing to conjugate prior assumptions we obtain a very efficient sampler for the regime allocation variable. A new hierarchical prior is introduced to allow for learning over different structural breaks. The model is extended to independent breaks in regression coefficients and the volatility parameters. Two empirical applications show the improvements the model has over benchmarks. In a macro application with seven variables we empirically demonstrate the benefits from moving from a multivariate structural break model to a set of univariate structural break models to account for heterogeneous break patterns across data series.     

A proposed method to evaluate the quality of services using Fuzzy sets theory

This paper seeks to provide the services sector with a focus on the assessment of quality and for this purpose, a technique that may able a quantitative approach to evaluating quality is proposed. The use of the fuzzy sets theory to process data was used, thus allowing a more flexible and suitable insight into the characteristics of the service sector. An extension of the technique for order performance by similarity to the ideal solution was used. This informs managers of the distance from the company $\prime $ s current level of quality, if compared to a company of perfect quality by means of an overall evaluation. The same technique was used to detect changes in the level of quality during the period surveyed by using a stratified assessment. Finally, a practical application of the approach proposed is presented.     

数据挖掘技术及其在生产过程质量控制中的应用

生产过程中质量控制对提高产品质量具有重要的意义。本文对数据挖掘技术的概念及方法进行了简单的介绍,以挤塑产品生产过程中温度的多元回归预测挖掘为例介绍了数据挖掘技术在生产过程质量控制的应用,表明这种方法在实际应用中的正确性和有效性。     

The contribution of the systems approach to planning

This paper attempts a three-part review and critique of the state of the application of systems theory to planning. The first part is a critique of McLoughlin's thesis expounded in his book Urban and Regional Planning. The critique emphasises the mechanistic bias and passivity assumed in McLoughlin's exposition and suggests directions in which the systems approach may be widened and strengthened to include in particular the political process and to include dynamic profiles of variables in the systems being analysed.The second part considers how far Chadwick in his book A Systems View of Planning has developed the approach, and comes to the conclusion that while the dynamics of the model are more rigorously treated the philosophical and social science assumptions, though hinted at, are not pursued to significantly greater lengths than by McLoughlin.The third part argues that while it is necessary to pursue the assumptions of the model, it is still permissible to continue model-building before the assumptions are finally tested and agreed as accurate, so long as accurate prediction on the basis of hypothetical assumptions is not taken in itself to constitute the proof of the accuracy of the assumptions.     

How to model multivariate extremes if one must?

In this paper we discuss some approaches to modeling extremely large values in multivariate time series. In particular, we discuss the notion of multivariate regular variation as a key to modeling multivariate heavy-tailed phenomena. The latter notion has found a variety of applications in queuing theory, stochastic networks, telecommunications, insurance, finance and other areas. We contrast this approach with modeling multivariate extremes by using the multivariate student distribution and copulas.