جستجوی مقالات مرتبط با کلیدواژه "fuzzy data" در نشریات گروه "ریاضی"
تکرار جستجوی کلیدواژه «fuzzy data» در نشریات گروه «علوم پایه»-
Considering intermediate data, two-stage networks eliminate the possibility of evaluating the performance of decision-making units in Black box mode. In this article, based on the structure of two-stage networks, Central Resource Allocation (CRA) models with fuzzy data are proposed. Then, two-stage network models are proposed in the form of combining data envelopment analysis and Raito analysis. In general, the models of this article using the CRA structure, Target introduce the decision-making units under the assumption of constant return to scale. CRA models in the two-stage network structure in DEA-R provide the possibility of finding suitable targets for decision-making units by solving a linear programming model instead of solving n linear programming problems (for n decision-making units). In conclusion, the models are proposed based on a practical study on 16 Iranian airlines.
Keywords: Data Envelopment Analysis, Fuzzy Data, Fuzzy Probability Function, DEA-RA -
International Journal Of Nonlinear Analysis And Applications, Volume:14 Issue: 1, Jan 2023, PP 2481 -2491In this article, two estimation methods are used to estimate the interval of the parameters for the inverse Weibull distribution in the case of fuzzy data. These two methods are based on, the maximum likelihood method and the relative maximum likelihood method. In addition, we compare the Maximum likelihood intervals with relative maximum likelihood intervals for both real and fuzzy data. The results of the comparison showed that the fuzziness interval estimation is better than the real one. Examples of applications are given.Keywords: fuzzy data, Inverse Weibull distribution, Interval Estimation
-
The EM algorithm is a powerful tool and generic useful device in a variety of problems for maximum likelihood estimation with incomplete data which usually appears in practice. Here, the term ``incomplete" means a general state and in different situations it can mean different meanings, such as lost data, open source data, censored observations, etc. This paper introduces an application of the EM algorithm in which the meaning of ``incomplete" data is non-precise or fuzzy observations. The proposed approach in this paper for estimating an unknown parameter in the parametric statistical model by maximizing the likelihood function based on fuzzy observations. Meanwhile, this article presents a case study in the electronics industry, which is an extension of a well-known example used in introductions to the EM algorithm and focuses on the applicability of the algorithm in a fuzzy environment. This paper can be useful for graduate students to understand the subject in fuzzy environment and moreover to use the EM algorithm in more complex examples.
Keywords: EM algorithm, Exponential distribution, Fuzzy Statistics, Fuzzy data, Maximum likelihood estimation -
Data Envelopment Analysis (DEA) is an extremely flexible and useful methodology, which provides a relative efficiency score for each decision-making unit.Classic DEA models only evaluate the performance efficiency of units and they cannot provide any information about the progress and regress of units in two periods. Also, they suppose that all inputs and outputs are positive real numbers. But in the real world, due to the existence of uncertainty, this assumption may not always be true. So, the DEA models with fuzzy data (FDEA models) can more realistically represent real-world problems than the conventional DEA models. In this paper, we assume all inputs, outputs and efficiency measures are triangular fuzzy numbers. So we present a method for obtaining the fuzzy Malmquist productivity index on fuzzy data, and finally, we can determine progress and regression for units with fuzzy data.
Keywords: DEA, fuzzy data, Fuzzy DEA, Malmquist Productivity Index -
The integration of data envelopment analysis (DEA) approach and Malmquist productivity index (MPI) is one of the popular and powerful techniques in order to calculate of changes in productivity of homogeneous decision making units (DMUs) over different time periods. In this paper, an extended Malmquist productivity index will be presented that is capable to be employed in the presence of fuzzy data and linguistic variables. It should be noted that possibilistic programming (PP) as well as chance-constrained programming (CCP) approaches are applied to handle data ambiguity. The implementation of the proposed fuzzy Malmquist productivity index (FMPI) is illustrated by a numerical example under triangular fuzzy data. Finally, the results show the applicability and efficacy of the extended MPI to calculate the changes of productivity of DMUs under fuzzy environment.Keywords: Malmquist Productivity Index, Data envelopment analysis, Possibilistic Programming, fuzzy data
-
Many real problems may often be reduced to solving a system of linear equations. However, in such real-world problems, the existence of uncertainties, like a fuzzy environment, is inevitable. Thus, the main aim of this paper is to propose a solving approach for a fuzzy linear system of equalities. The presented approach is based on the nearest weighted approximation of fuzzy numbers. Two numerical examples illustrate the capabilities of the proposed approach.
Keywords: Linear system, Fuzzy data, Nearest approximation -
International Journal Of Nonlinear Analysis And Applications, Volume:13 Issue: 1, Winter-Spring 2022, PP 3723 -3732
In this paper, the intuitionistic fuzzy set and the triangular intuitionistic fuzzy number were displayed, as well as the intuitionistic fuzzy semi-parametric logistic regression model when the parameters and the dependent variable are fuzzy and the independent variables are crisp. Two methods were used to estimate the model on fuzzy data representing Coronavirus data, which are the suggested method and {The Wang et al method}, through the mean square error and the measure of goodness-of-fit, the suggested estimation method was the best.
Keywords: intuitionistic fuzzy set, the triangular intuitionistic fuzzy number, fuzzy data, mean square error, goodness-of-fit -
در برخی از تحقیقات، پژوهشگران به مطالعه و تخمین برخی از پارامترهای تاثیرگذار بر اندازه کارایی از جمله میزان ورودی یا میزان خروجی یک DMU پرداخته اند بطوریکه اندازه کارایی حفظ یا به میزان معینی بهبود یابد .این دسته از مسایل تحت عنوان تحلیل پوششی داده های معکوس در ادبیات تحلیل عملکرد مورد مطالعه قرار می گیرند. این مقاله به مطالعه تحلیل پوششی داده های معکوس می پردازد. مسیله تخمین ورودی یا خروجی با بهبود در اندازه کارایی واحد، مورد بررسی قرار گرفته است.لذا در این مقاله، با افزایش سطح ورودی های نامطلوب وسطح خروجی های مطلوب واحدهای تصمیم گیری به همراه بهبود در کارایی که مورد نظر تصمیم گیرنده می باشد، میزان تغییرات سطح ورودی های مطلوب و سطح خروجی های نامطلوب تخمین زده می شود. برای این منظور، با در نظر گرفتن داده ها به صورت بازه ای، روش DEA معکوس را با استفاده از مدل برنامه ریزی خطی چندهدفه (MOLP)، به کار می گیریم، به طوری که کارایی واحد تحت ارزیابی بهبود پیدا کند. در ادامه با یک مثال کاربردی روش پیشنهادی مورد بحث و بررسی قرار می گیرد.کلید واژگان: DEA, IDEA, MOLP, داده های بازه ای, داده های نامطلوبSome researchers deals to estimation of some of the influences factors in efficiency score in which the DMU, maintains or improves its current efficiency level. This problems are considered in a general framework which is called inverse DEA. This paper studies the inverse data envelopment analysis. The issue of input or output estimation has been examined with improvement in unit efficiency.Therefore, in this paper, by increasing the level of undesirable inputs and the level of desired outputs of decision-making units along with improvements in the efficiency of the decision-maker, the level of changes in the level of desired inputs and the level of undesirable outputs is estimated. To do this, we consider the data as Interval data and then use the inverse DEA method using a multi-objective linear programming model (MOLP), so that the unit performance under evaluation is improved. The following is an applied example of the proposed method.Keywords: DEA, MOLP, IDEA, fuzzy data, Undesirable data
-
ممکن است در آزمون فرضیه ها با مواردی روبرو شویم که داده ها به صورت مبهم / فازی ثبت شده باشند. در چنین شرایطی، روش های کلاسیک آزمون فرضیه ها قادر به حل این مسئله جدید نیستند و نیاز به تعمیم دارند. در حل مسئله آزمون فرضیه ها بر اساس داده های فازی، ابهام موجود در داده ها منجر به ایجاد ابهام در p-مقدار میشود. این مقاله به محاسبه p-مقدار فازی بر اساس داده های فازی-مقدار مبتنی بر اصل گسترش میپردازد. همچنین، با توجه به اینکه روش p-مقدار متداولترین روش آزمون فرضیه ها در بین کاربران علوم مختلف است، پس دو مطالعه موردی مبتنی بر p-مقدار فازی در این مقاله نیز ارایه شده است. اولین مطالعه موردی درباره «داده های فازی ثبت شده به وسیله یک دوربین سرعت سنج» و دومین مطالعه درباره »طول عمر باتریهای تولیدی یک کارخانه« است که هر دو با استفاده از رویکرد جدید مورد بررسی قرار گرفته اند.
In testing hypotheses, we may confront with cases where data are recorded as non-precise (fuzzy) rather than crisp. In such situations, the classical methods of testing hypotheses are not capable and need to be generalized. In solving the problem of testing hypotheses based on fuzzy data, the fuzziness of the observed data leads to the fuzzy p-value. This paper has been focused to calculate fuzzy p-value based on fuzzy data using the extension principle. Also, considering that p-value method is the most widely used / popular approach for testing hypotheses among different sciences users, two fuzzy $p$-value-based case studies have been provided in this paper. The first case study is discussed on ″the fuzzy data from a speedometer camera" and the second is deliberate about ″the lifetime of produced batteries in a factory" and both of them have been solved by a novel approach considering other studies found in the literature.
Keywords: Testing hypotheses, fuzzy decision, fuzzy p-value, extension principle, fuzzy significance level, fuzzy data -
Domestic wastewater treatment covers the processes that used to treat waters that have been polluted by commercial or anthropogenic activities prior to its discharge into the environment or its re-use. Data Envelopment Analysis was applied to evaluate six urban wastewater treatment plants efficiency in three stages with fuzzy data and discretionary and non- discretionary inputs in Iran. Stream flow, Chemical Oxygen Demand and Total Suspended Solids were considered as input variables. In this paper a fuzzy network Data Envelopment Analysis model is proposed and solved with GAMS software. Results showed that showed that Bandar Torkman has the best performance between these waste water treatment plants and Babol and yasreb waste water treatment plants do not operate well.Keywords: Data Envelopment Analysis, urban wastewater treatment, fuzzy data, efficiency in three stage system, discretion, non-discretion inputs
-
This paper deals with the problem of testing statistical hypotheses when the available data are fuzzy. In this approach, we first obtain a fuzzy test statistic based on fuzzy data, and then, based on a new signed distance between fuzzy numbers, we introduce a new decision rule to accept/reject the hypothesis of interest. The proposed approach is investigated for two cases: the case without nuisance parameters and the case with nuisance parameters. This method is employed to test the hypotheses for the mean of a normal distribution with known/unknown variance, the variance of a normal distribution, the difference of means of two normal distributions with known/unknown variances, and the ratio of variances of two normal distributions.Keywords: Fuzzy data, Fuzzy test statistic, Signed distance, Statistical hypothesis, Testing hypothesis
-
Traditional data envelopment analysis (DEA) models usually evaluate the efficiency scores of decision making units (DMUs) with precise data from an optimistic point of view where the status of each measure (i.e. input/output) is certain. However, there are occasions in real world applications that measures can play both input and output roles in an imprecise environment. In the current study, measures with two roles, input and output, are called “adaptable measures”. This paper proposes a DEA-based approach for estimating the performance of DMUs where adaptable and fuzzy data exist. Indeed, efficiency scores are calculated from two aspects, optimistic and pessimistic, when there are adaptable and fuzzy data. Two different efficiency scores are integrated into a geometric average efficiency. Thus, the overall efficiency is calculated and adaptable variables are split into input and output variables in evaluating the efficiency of each DMU. A numerical example is used to illustrate the approach.
Keywords: Data envelopment analysis (DEA), Efficiency, fuzzy data, Adaptable variable -
Data envelopment analysis (DEA )has been extended to cross -efficiency evaluation for ranking decision making units (DEA) and eliminating unrealistic weighting schemes.Unfortunately,the nonunique optimal weights problem in DEA has reduced the usefulness of this extended method.Aiming at solving this problem,we first incorporate a target idenification model to get reachable targets for all the DMUs. Then, several secondary goal models are proposed for wights selection considering both desirable and undesirable cross-efficiency targets of all the DMUs. Compared with the traditional secondary goal models, the cross-efficiency targets are improved in that all targets are always reachable for the DMUs. In addition, the proposed models considered the DMUs, willingness to get close to their desirable cross-efficiency targets and to avoid their undesirable cross-efficiency targets simultaneously while the traditional secondary goal models considered only the ideal targets of the DMUs. Since usually some detailed data are available, and they have to figure range. In this paper we extend this model and secondary goals so that is able to calculate the cross efficiency of these conditions.
Keywords: Data Envelopment Analysis, efficiency of cross-secondary goals, fuzzy data -
Measurement results contain different kinds of uncertainty. Besides systematic errors and random errors individual measurement results are also subject to another type of uncertainty, so-called emph{fuzziness}. It turns out that special fuzzy subsets of the set of real numbers $RR$ are useful to model fuzziness of measurement results. These fuzzy subsets $x^*$ are called emph{fuzzy numbers}. The membership functions of fuzzy numbers have to be determined. In the paper first a characterization of membership function is given, and after that methods to obtain special membership functions of fuzzy numbers, so-called emph{characterizing functions} describing measurement results are treated.Keywords: Characterizing function, Fuzzy data, Generating families, Measurement results, Vector, characterizing function
-
Data Envelopment Analysis (DEA) is a technique for measuring the efficiency of a set of Decision Making Units (DMUs) with common data, but in general it is not practical. This paper presents a framework where DEA is used to measure overall profit efficiency with fuzzy data. Specifically, it is shown that as the inputs, outputs and price vectors are fuzzy numbers, the DMUs cannot be easily evaluated. Thus, presenting a new method for computing the efficiency of DMUs with fuzzy data will be benefic. Also, it presents where DEA is used to measure overall profit of efficiency with interval and fuzzy inputs and outputs and an interval will be defined for the efficiency. The proposed method give the best and the worst overall profit efficiency for DMUs. The method is illustrated by solving numerical examples.
Keywords: Data Envelopment Analysis, fuzzy Data, interval Data, overall Profit Efficiency
- نتایج بر اساس تاریخ انتشار مرتب شدهاند.
- کلیدواژه مورد نظر شما تنها در فیلد کلیدواژگان مقالات جستجو شدهاست. به منظور حذف نتایج غیر مرتبط، جستجو تنها در مقالات مجلاتی انجام شده که با مجله ماخذ هم موضوع هستند.
- در صورتی که میخواهید جستجو را در همه موضوعات و با شرایط دیگر تکرار کنید به صفحه جستجوی پیشرفته مجلات مراجعه کنید.