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Although control charts can notify the state of out-of-control in a process by generating a signal, the indication is usually followed by a considerable amount of delay. Identifying the real time of the change in a process would provide a starting point for further investigation of an assignable cause. This paper addresses the problem of detecting the change point in different processes when the quality characteristics drift steadily away from an in-control state. For this purpose, a fuzzy statistical clustering (FSC) method is used to estimate the drift time in different processes. Since the application of an FSC method requires both in- and out-of-control values of the process parameter, a linear regression model is utilised to estimate the trend rate and then calculate the out-of-control process parameter. Through extensive simulations, the performance of the proposed change point estimation method is analysed and compared with the most recent estimators for several control charts. The results demonstrate that the proposed method is more effective in detecting the drift time through a wide range of trend rates. Furthermore, it is shown that the proposed method offers a higher estimation precision compared to conventional statistical methods. [ABSTRACT FROM AUTHOR]

Industrial robots, which enable manufacturing firms to produce high-quality products in a cost-effective manner, are important components of advanced manufacturing technologies. The performance of industrial robots is determined by multiple and conflicting criteria that have to be simultaneously considered in a robust selection study. In this study, a decision model based on fuzzy linear regression is presented for industrial robot selection. Fuzzy linear regression provides an alternative approach to statistical regression for modelling situations where the relationships are vague or the data set cannot satisfy the assumptions of statistical regression. The results obtained by employing fuzzy linear regression are compared with those of earlier studies applying different analytical methods to a previously reported robot selection problem. [ABSTRACT FROM PUBLISHER]

Agility is the performance measure of agile manufacturing (AM) practices and with the increasing adoption of AM systems its assessment has gained in importance. This study has been undertaken to fill the observed research gap that no research has been attempted to validate an agility assessment result using an alternative effective method. The agility assessment of an Indian electric automotive car manufacturing organisation has been done using a scoring approach and validated using an effective multi-grade fuzzy method. The result indicates that the organisation was agile to an extent of 84.1% using the scoring approach and 7.05 using the multi-grade fuzzy method, which implies the organisation is agile. The gap analysis results indicate that the largest gap is observed in the case of ‘nature of management’ criterion followed by ‘devolution of authority’, ‘customer response adoption’ and ‘employee involvement’. Necessary actions were taken for the improvement of these agility gaps. The improvement in agile performance measures is 7.7 to 9.7 (on a Likert scale of range 0–10) after the implementation of the suggested proposals. The statistical validation study indicates the feasibility of improvement in agility after the assessment exercise with a practical success rate of 90%. [ABSTRACT FROM PUBLISHER]

The premise of dealing with uncertainty in this article is to figure out the impact of uncertainties on project performance. This article proposes a framework for quantitative analysis that combines a fuzzy mathematics approach with system dynamics theory. Simulations generated using a model based on the framework show that uncertainties affect project performance by changing project parameters, and the mechanisms involved depend on the sources of the uncertainties. Furthermore, while technical and financial uncertainties mainly influence project costs, uncertainties related to markets, regulations, and organizational structures affect both project duration and cost. Finally, strategies for coping with uncertainties in a resource allocation perspective are presented. [ABSTRACT FROM AUTHOR]

In this paper, the classical statistical test based on intuitionistic fuzzy hypotheses in relation to the underlying population parametric is extended. In this approach, the type-I, type-II, power of test, andp-value are extended for intuitionistic fuzzy hypotheses. Throughout the paper, some applied examples are provided for both parametric and non parametric cases to clarify the discussions. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive mutation particle swarm optimization (AMPSO) to realize multi-objective optimization design method through scale-based product platform theory model. The Pareto-optimal solution was obtained via AMPSO, then the fuzzy statistics algorithm is presented to extract the optimal solution of multi-objective optimization problem. The Multi-objective Optimization Method was carried out in two stages. In the first stage, each product is optimized independently via AMPSO and the product platform constant parameter and its value is obtained according to the change ratio of design variables; In the second stage, the scaling variables of each product are solved via AMPSO based on the optimization objectives improving the performance in constraint of restrictions and the best compromise solution is extracted based on fuzzy statistics algorithm. [ABSTRACT FROM AUTHOR]

The food retailers have to make their supply chains more customer-driven to sustain in modern competitive environment. It is essential for them to assimilate consumer’s perception to improve their market share. The firms usually utilise customer’s opinion in the form of structured data collected from various means such as conducting market survey, customer interviews and market research to explore the interrelationships among factors influencing consumer purchasing behaviour and associated supply chain. However, there is abundance of unstructured consumer’s opinion available on social media (Twitter). Usually, retailers struggle to employ unstructured data in above decision-making process. In this paper, firstly, by the help of literature and social media Big Data, factors influencing consumer’s beef purchasing decisions are identified. Thereafter, interrelationships between these factors are established using big data supplemented with ISM and Fuzzy MICMAC analysis. Factors are divided as per their dependence and driving power. The proposed frameworks enable to enforce decree on the intricacy of the factors. Finally, recommendations are prescribed. The proposed approach will assist retailers to design consumer centric supply chain. [ABSTRACT FROM PUBLISHER]

Life time data analysis is regarded as one of the significant out-shoots of statistics. Classical statistical techniques reckon life time observations as precise numbers and solely cover variation among the observations. In fact, there are two types of uncertainty in data: variation among the observations and the fuzziness. To this effect, the analysis techniques, which do not consider fuzziness and are only based on precise life time observations, use incomplete information; hence lead to pseudo results. This study aimed at generalizing parameters estimation, survival functions, and hazard rates for fuzzy life time data. [ABSTRACT FROM AUTHOR]

The case of nonresponse in multivariate stratified sampling survey was first introduced by Hansen and Hurwitz in 1946 considering the sampling variances and costs to be deterministic. However, in real life situations sampling variance and cost are often random (stochastic) and have probability distributions. In this article, we have formulated the multivariate stratified sampling in the presence of nonresponse with random sampling variances and costs as a multiobjective stochastic programming problem. Here, the sampling variance and costs are considered random and converted into a deterministic NLPP by using chance constraint and modified E-model. A solution procedure using three different approaches are adopted viz. goal programming, fuzzy programming, and D1distance method to obtain the compromise allocation for the formulated problem. An empirical study has also been provided to illustrate the computational details. [ABSTRACT FROM PUBLISHER]

Service quality (SERVQUAL) is one of the most important factors that increase the usage of public transportation. SERVQUAL improvements can solve many problems such as traffic congestion, air and noise pollution, and energy consumption in any public transportation system (PTS). In this paper, a hybrid fuzzy approach consisting of SERVQUAL, the Delphi method, fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methodologies for the evaluation of SERVQUAL of PTS is presented. The SERVQUAL method is used for classifying the public transportation SERVQUAL evaluation criteria, the Delphi method is used to obtain the evaluations of experts, fuzzy AHP is used to determine the weights of the criteria and fuzzy TOPSIS is used to rank the alternatives. A new linguistic scale is taken into consideration while converting the linguistic evaluations of survey respondents to fuzzy numbers. The proposed approach is applied for the PTS in Istanbul, and the public transportation company that provides the highest customer satisfaction is determined. With this aim, four alternative companies named Istanbul Electric Tramway and Tunnel (IETT), Metrobus, Private Public Transportation Buses and Otobus Inc. are evaluated. [ABSTRACT FROM PUBLISHER]

Analysis of variance (ANOVA) is an important method in exploratory and confirmatory data analysis. The simplest type of ANOVA is one-way ANOVA for comparison among means of several populations. In this article, we extend one-way ANOVA to a case where observed data are fuzzy observations rather than real numbers. Two real-data examples are given to show the performance of this method. [ABSTRACT FROM AUTHOR]

Most of the traditional methods for assessing the capability of manufacturing processes are dealing with crisp quality. In quality control, such as other statistical problems, we may confront imprecise concepts. One case is a situation in which specification limits (SLs) are imprecise. In this situation, the fuzzy process capability indices (PCIs) [image omitted], [image omitted] and [image omitted] are necessary for measuring the fuzzy quality in an in-control process. These fuzzy capability indices are also helpful for comparing manufacturing processes with SLs. The fuzzy capability index [image omitted] is used to provide an assessment of the ability of the fuzzy process to be clustered around the target value. The emphasis on the use of [image omitted] over the other two fuzzy indices, [image omitted] and [image omitted], is due to its definition that provides indications of both the process variability and deviation of process mean from a specified target. In this article, by using triangular fuzzy SLs, we present four approximate [image omitted] fuzzy confidence regions for the fuzzy PCI [image omitted]. A numerical example is given to show the performance of the method. [ABSTRACT FROM AUTHOR]

Regional validation studies of global land cover products are usually based on medium-resolution imagery and auxiliary high-resolution information. Because of the heterogeneity of landscapes at 500-1000 m resolution and the characteristics of the reference sites/data, the accuracy of the results may depend on the methodology used. This work proposes an integration scheme for the regional validation of global products. The assessment produces an accuracy index with possibility margins, representing various levels of positional, thematic and temporal tolerance. The methodology is used to evaluate MOD12Q1 on 1 million km2 of the land cover in Mexico, with emphasis on the diversity of forest types/phenologies. The area of higher confusion is reported in deciduous tropical dry forests, which tend to be classified as evergreen forests, and in oak forests, which tend to be classified as grassland. The index could be used as a tool for the consistent comparison between validation results among regions. [ABSTRACT FROM AUTHOR]

In quality control, we may confront imprecise concepts. One case is a situation in which upper and lower specification limits (SLs) are imprecise. If we introduce vagueness into SLs, we face quite new, reasonable and interesting processes, and the ordinary capability indices are not appropriate for measuring the capability of these processes. In this paper, similar to the traditional process capability indices (PCIs), we develop a fuzzy analogue by a distance defined on a fuzzy limit space and introduce PCIs, where instead of precise SLs we have two membership functions for upper and lower SLs. These indices are necessary when SLs are fuzzy, and they are helpful for comparing manufacturing process with fuzzy SLs. Some interesting relations among these introduced indices are proved. Numerical examples are given to clarify the method. [ABSTRACT FROM AUTHOR]

The aim of this paper is to extend the concept of inverse of a matrix with fuzzy numbers as its elements, which may be used to model uncertain and imprecise aspects of real-world problems. We pursue two main ideas based on employing real scenarios and arithmetic operators. In each case, exact and inexact strategies are provided. In the first idea, we give some necessary and sufficient conditions for invertibility of fuzzy matrices based on regularity of their scenarios. And then Zadeh's extension principle and interpolation on Rohn's approach for inverting interval matrices are followed to compute fuzzy inverse. In the second idea, Dubois and Prade's arithmetic operators will be employed for the same purpose. But with respect to the inherent difficulties which are derived from the positivity restriction on spreads of fuzzy numbers, the concept of ε-inverse of a fuzzy matrix and its relaxation are generalized and some useful theorems will be revealed. Finally fuzzifying the defuzzified version of the original problem for introducing fuzzy inverse, which can be followed by each idea, will be presented. [ABSTRACT FROM AUTHOR]

The problem of defuzzification is examined in this paper from a broader perspective as a special way of dealing with the general problem of retranslation. The paper includes an overview of different formulations of the problem of defuzzification, as well as an overview of methods that have been suggested in the literature for dealing with the problem. Our own approach to defuzzification, which is described in the paper in more details, is based on relevant measures of uncertainty-based information. This paper is a companion to our recent paper that addresses the general problem of retranslation in computing with perceptions (Martin and Klir 2006). [ABSTRACT FROM AUTHOR]

This paper proposes an automatic image segmentation algorithm. Our hierarchical algorithm uses recursive segmentation that consists of two major steps. First, local thresholding is carried out by the fuzzy hit-or-miss operator, which allows dynamic separation of a grey-scale image into two classes, based on local intensity distributions. The fuzzy hit-or-miss, being an operator of fuzzy mathematical morphology, plays an important role in performing the dynamic local segmentation. This operator gives a better shape description than global thresholding methods. It also retains small but significant regions in satellite images. Second, the homogeneity index is measured in each class based on the quality of normalized intra-region uniformity. The proposed method has been tested using both synthetic and satellite images successfully; moreover, the algorithm can estimate the number of classes automatically. [ABSTRACT FROM AUTHOR]

Image classification of historical aerial photographs is very useful for the study of medium-to-long term (10–50 years) vegetation changes. To determine the quality of information derived from the classification process, accuracy assessment of the classification is implemented. Error matrix, which is primarily used in remote sensing for accuracy assessment, is typically based on an evaluation of the derived classification against some ‘ground truth’ or reference dataset. Regrettably ‘ground truths’ for some old historical photographs are rarely available. To solve this problem we formulate, in this Part I, methods of classification of aerial photographs and computation of accuracy assessment parameters of classification products in the absence of ground data, using the fuzzy classification technique. In addition, since point estimates of these accuracy parameters require associated standard errors, in order to be useful for statistical analysis, the method of computation of standard errors of accuracy measures using the bootstrap resampling techniques is presented. These methods are tested with historical aerial photographs, of part of Adulam Nature Reserve, Israel, spanning a period of 51 years. Results illustrate the applicability and efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

The notion of coherent systems plays an essential role in conventional reliability theory. A system is said to be coherent if all of its components are relevant and the system reliability is improved as the component reliabilities are improved. However, in many complex systems or networks, not all the components are unconditionally relevant. As a result, in this paper we introduce the notion of variable-structure coherent systems to describe those systems that extensively exist and demonstrate essentially distinct features not observed in conventional coherent systems. A variable-structure coherent system consists of a number of substructures that are each a coherent system in conventional sense themselves. We then analyze the structural properties of variable-structure coherent systems; define the system operational profile, the system reliability, and the system structural profile. We study the system life distribution, the substructure importance, and the component importance. Finally, we deal with phase-cyclic systems in the context of variable-structure coherent systems. [ABSTRACT FROM AUTHOR]

The goal of the training evaluation in a simulation is to provide feedback about the user performance in the training environment. Approaches to online or offline evaluation of training in simulators based on virtual reality have been proposed. An online evaluator must have low complexity algorithm to not compromise the performance of the simulator. A new approach to quality of training online evaluation in virtual reality worlds is proposed in this work. This approach uses fuzzy hidden Markov models (FHMM) for modeling and classification of trainee in pre-defined classes of training. [ABSTRACT FROM AUTHOR]

A correction is presented for the article "A Performance Analysis and Comparison of Different Fuzzy Inference Models for Advanced Prediction of Reservoir Properties," by E. Ghotbi Ravandi which was published in the volume 34, number 1 issue.