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The purpose of this paper is to construct innovative exact and approximative sampling plans for acceptance sampling in statistical quality control. These sampling plans are determined for crisp and fuzzy formulation of quality limits, various lot sizes and common α- and β-levels.

The authors use generalized fuzzy hypothesis testing to determine sampling plans with fuzzified quality limits. This test method allows a consideration of the indifference zone related to expert opinion or user priorities. In addition to the exact sampling plans calculated with the hypergeometric operating characteristic function, the authors consider approximative sampling plans using a little known, but excellent operating characteristic function. Further, a comprehensive sensitivity analysis of calculated sampling plans is performed, in order to examine how the inspection effort depends on crisp and fuzzy formulation of quality limits, the lot size and specifications of the producer’s and consumer’s risks.

The results related the parametric sensitivity analysis of the calculated sampling plans and the conclusions regarding the approximation quality provide the user a comprehensive basis for a direct implementation of the sampling plans in practice.

The constructed sampling plans ensure the simultaneous control of producer’s and consumer’s risks with the smallest possible inspection effort on the one hand and a consideration of expert opinion or user priorities on the other hand.

The purpose of this paper is to present the perception of service quality. The measurement of service quality has grown significantly in importance over recent years. However, since customers' perceptions of service quality are expressed subjectively in vague linguistic terms therefore, a fuzzy approach to service quality of an academic library is applied and the gap between the student's expectations and perceptions analyzed.

The LibQUAL method is applied to identify the gap between customer expectation and perceptions of the actual service received. The linguistic procedure is developed considering four dimensions of LibQUAL. The quality analyses are based on the non‐parametric rank‐based statistics (Wilcoxon's two‐sample rank sum statistic) and extended to fuzzy data which consequently reinforces results in comparison with crisp data.

It is found that there is no gap between the students' expectations and perceptions in three dimensions of “affect of service”, “library as a place”, and “personal control”. But, from the students' point of view, regarding the “access to information” dimension, there is a significant difference between the students' expectations and perceptions.

The instrument that is administered is a paper questionnaire version based on the 2002 model of LibQUAL.

It is felt that application of this fuzzy procedure has implications on possible constraints on the statistics of interest, as well as on the p‐value of any associated significance tests.

This paper is possibly the first to demonstrate the application of fuzzy logic to the LibQUAL model. This fuzzy procedure persuades possibility constraints on the statistic of interest, as well as on the p‐value of an associated significance test. This fuzzy procedure persuades possibility constraints on the statistic of interest, as well as on the p‐value of an associated significance test.

The principal aim of this study was to provide more realistic output data based on imprecise measurements of product quality. The real‐world problems in fuzzy testing and selecting better processes performance are considered.

The Taguchi index, which provides numerical measures on process performance, has been widely used in the industry. In practice, the Taghchi index is estimated by sample data, thus it is of interest to obtain the confidence limits of the estimate C_pm for assessing processes. In addition, it is much more realistic, because in general the output quality characteristics of continuous quantities are more or less imprecise. Using the approach taken by Buckley, with some extensions, a general method is used to combine the vector of fuzzy numbers to produce the membership function of a fuzzy estimator of C_pm for further fuzzy testing and selection of better process performances.

As the rapid advancement of manufacturing technology occurs, current firms are increasing their levels of out sourcing and are relying more heavily on their supply chain as a source of their competitive advantage. Supplier selection decisions have become an important component of production and logistics management. Those decisions have a significant impact on manufacturers' ability to compete as purchases from outside suppliers may account for a large proportion of a product's costs.

The authors assume that measurements are taken from normally distributed populations in this research. Using fuzzy inference to assess manufacturing process capability processed using imprecise data under mild and severe departures from normality would be an interesting issue for further research.

From a managerial standpoint, considering stochastic uncertainty and fuzziness of data during testing and selecting the better supplier often provides a strong incentive to suppliers to adhere to the conscious gathering of data and variance reductions, as well as to quality requirements and standards.

An obvious advantage of process capability analysis over traditional classical approaches, which use binomial distribution for estimating low fractions of NC, is that reviewing a smaller sample reduces time, effort, and expenses.

The purpose of this study is to evaluate two supply chain resilience key elements of vulnerability and capability in the automotive industry.

We propose a fuzzy approach for statistical hypothesis testing and analyze two research hypotheses by synthesizing the results of a questionnaire given to 44 companies in the Iranian automotive industry.

The results indicate that the automotive industry in Iran should: (1) resist five elements of vulnerability, i.e. “external pressures,” “sensitivity,” “connectivity,” “supplier/customer disruptions,” and “resource limits,” and (2) embrace nine elements of capability, i.e. “flexibility in order fulfillment,” “capacity,” “efficiency,” “visibility,” “adaptability,” “recovery,” “dispersion,” “organization,” “market position” and “security” to achieve greater resiliency elasticity in the supply chain.

This is the first study on the supply chain resilience vulnerabilities and capabilities in the Iranian automotive industry.

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.

The purpose of this paper is to establish correlations among the input variables of production within themselves and input variables of consumption within themselves and to forecast the production and consumption of the rice.

The production and consumption of rice crop is governed by diverse variables. In the present study five key input variables for production of rice based on literature review and the authenticated data available from agricultural sources have been selected. These variables are area sown, agricultural workers (AW), area irrigated, growth rate and yield per hectare. On similar basis four key input variables responsible for consumption of rice are considered, namely, price of rice, population, poverty ratio and per capita net national product (NNP).

Correlation analysis showed that priority wise production of rice depends upon yield per hectare, percentage irrigation, AW and area sown. The growth rate is found to be having insignificant correlation with other variables of production and hence was omitted from subsequent study. Correlation analysis also showed that priority wise consumption depends upon whole sale price per ton, population and the per capita NNP. The poverty ratio is found to be having insignificant correlation with other variables of consumption and hence was omitted from subsequent study. The outcomes of the correlation analysis are utilized for designing rule base for fuzzy inference system (FIS) to forecast the production and consumption of the rice. Subsequently Bayesian technique is used to forecast production and consumption and its results are compared with the results of fuzzy inference analysis.

There are many techniques used for forecasting purpose but FIS and Bayesian technique outperform others. In the present study, the authors therefore focussed on these two techniques. Bayesian technique takes into account the expert opinion at the current conditions whereas FIS uses previously designed rule base. Besides discussing the appropriateness of these two techniques for forecasting production and consumption of rice, their forecasting outcomes will help in logistical and operational planning of the resources at national level, farmers’ level and traders’ level.

The traditional literature dealing with statistical decision problems usually assumes that previous information about an associated experiment may be expressed by means of conditional probabilistic information, and the actual experimental outcomes can be perceived with exactness by the statistician. We now consider statistical decision problems satisfying the first assumption above, so that the actual available information cannot be exactly perceived, but rather it may be assimilated with fuzzy information (as defined by Zadeh et al.).

In the digital era, the dynamics of the communication process in classrooms have changed significantly. With the help of computer-mediated communication techniques, especially animation, teachers can deliver a distinct learning experience to students that can be fun, while presenting complex ideas in simpler forms. The purpose of this paper is to assess the effectiveness of animation in education, in the context of developing Asian countries, using Karnataka, an Indian state, as a study area.

This paper uses the field experimental method to assess animation’s effectiveness in education. Attempts are made to neutralize the influence of extraneous factors, such as psychological conditions, and the socio-economic background of students, while assessing academic performance. To achieve this, a fuzzy-set-theory-based two-sample statistical hypothesis test is used.

Results indicate that animation can be used as an effective tool for communication in pedagogy and, if used properly, can improve students’ academic performance in primary education, even in developing countries such as India.

The paper’s limitations are explored, and point to how future research could use more advanced statistical tools to identify the motivational, behavioral, cognitive and psychological factors influencing students, when animation is used in education, and should perform a comparative analysis of the performance of students in developed and developing countries.

As the current study proves that animation is effective in education, even in developing countries such as India, efforts should be made to convert existing curricula into animated multimedia content. Currently, most government-run schools in India use traditional chalk-and-talk methods for teaching. The use of animated instructional material will help improve the standard of educational communication in classroom, and maintain consistency in delivering the curriculum.

Constructing a fuzzy control chart with interval-valued fuzzy data is an important topic in the fields of medical, sociological, economics, service and management. In particular, when the data illustrates uncertainty, inconsistency and is incomplete which is often the. case of real data. Traditionally, we use variable control chart to detect the process shift with real value. However, when the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional statistical process control (SPC) to monitor the fuzzy control chart. The purpose of this paper is to propose the designed standardized fuzzy control chart for interval-valued fuzzy data set.

The general statistical principles used on the standardized control chart are applied to fuzzy control chart for interval-valued fuzzy data.

When the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional SPC to monitor the fuzzy control chart. This study proposes the designed standardized fuzzy control chart for interval-valued fuzzy data set of vegetable price from January 2009 to September 2010 in Taiwan obtained from Council of Agriculture, Executive Yuan. Empirical studies are used to illustrate the application for designing standardized fuzzy control chart. More related practical phenomena can be explained by this appropriate definition of fuzzy control chart.

This paper uses a simpler approach to construct the standardized interval-valued chart for fuzzy data based on traditional standardized control chart which is easy and straightforward. Moreover, the control limit of the designed standardized fuzzy control chart is an interval with (LCL, UCL), which consists of the conventional range of classical standardized control chart.