DEVELOPING A NEW APPROACH FOR EVALUATION OF BUSINESS PROCESSES IN A FUZZY ENVIRONMENT

Evaluation of business processes plays a significant role in business development and improvement. Therefore, organizations need a systematic approach to evaluate all the changes through robust and powerful techniques that can formulate the relationship between the available information and the degree of the inherent uncertainty. In this paper, a set of operational variables are defined. Then, the SPSS software package is utilized to validate the gathered data. After that, the variables are categorized by the use of a clustering technique. Finally, five major factors are determined as the most effective components. According to the inherent uncertainty involved in the process of modelling, fuzzy set theory, a powerful mathematical tool is applied to handle the vagueness. In order to construct a knowledge base based on the fuzzy set theory, the linguistic concepts for each variable are defined. Lastly, membership functions are described and a set of fuzzy rules based on input-output parameters are written in MATLAB software environment. To demonstrate the potential application of the proposed approach, a real case study is illustrated. The results reflect the capability and effectiveness of the approach proposed in this paper.

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Optimum fuzzy based approach to improve the instrument’s performance affected by environmental conditions

Serbian Journal of Electrical Engineering ◽

10.2298/sjee121219006v ◽

2013 ◽

Vol 10 (2) ◽

pp. 309-318

Author(s):

Avnesh Verma ◽

Tiwari Anand

Keyword(s):

Set Theory ◽

Fuzzy Set ◽

Environmental Conditions ◽

Fuzzy Set Theory ◽

Operating Conditions ◽

Mathematical Tool ◽

Optimal Operating ◽

Independent Variables ◽

Humidity Index ◽

Two Independent Variables

The performance of instrument has been analyzed, considering the ideal conditions and results which have been obtained when the instrument is subjected to diversified combination of environmental conditions. A peerless analysis has been carried out of these environmental conditions using 'fuzzy set theory' as a mathematical tool. The results showed how the use of fuzzy set theory is adequate to analyze environmental conditions and is able to suggest the optimal operating conditions for performance of the instrument. In this analysis two independent variables temperature and relative humidity have been used, and based on these two independent variables a third dependent variable was defined namely temperature humidity index (THI). Based on THI, a set of fuzzy rules were established considering the influence of both independent variables. The results obtained without fuzzy experimentation according to the specification of instrument and results obtained with fuzzy analysis shown were quite comparable. The results obtained after fuzzification explicitly show that the operating instrument?s accuracy can be predicted by comparing with the THI zone and at the same time this research gives an insight for selection of nearest optimal operating condition for normal working of the instrument. It can be summarized that the abrupt variation in independent variables can make the instrument unstable and the fuzzy based approach is helpful in improving the overall instrument performance.

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Fuzzy set theory-based model for identifying the potential of improving process KPIs in production logistics area

New Trends and Issues Proceedings on Humanities and Social Sciences ◽

10.18844/prosoc.v4i10.3098 ◽

2018 ◽

Vol 4 (10) ◽

pp. 327-336

Author(s):

Denisa Hrusecka

Keyword(s):

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Performance Indicators ◽

Business Processes ◽

Fuzzy Model ◽

Real Data ◽

Key Performance Indicators ◽

Production Logistics ◽

Low Performance

The high complexity of today’s manufacturing environment brings many problems with planning and managing, especially production, logistic and other key business processes. In many cases, it is quite complicating to identify the real causes of problems that enterprises face or to decide which one of them should be solved first. Especially, in the case of large enterprises, it is complicating to access expertise among all departments and employed professionals in order to solve the problems most efficiently. Our fuzzy model provides a simple tool for easy identification of the most significant problems of observed processes that cause their low performance according to the measured values of their key performance indicators. The model is based on data gained through interviews with production managers, industry experts and other professionals, and verified by real data from a model company. The results are presented in the form of case studies in this contribution. Keywords: Production logistics, key performance indicators, KPI, productivity, problem identification, fuzzy set theory, process.

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Fuzzy set theory–based model for identifying the potential for improving process KPI in production logistics area

New Trends and Issues Proceedings on Humanities and Social Sciences ◽

10.18844/prosoc.v4i10.3086 ◽

2018 ◽

Vol 4 (10) ◽

pp. 154-163

Author(s):

Denisa Hrusecka

Keyword(s):

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Performance Indicators ◽

Business Processes ◽

Fuzzy Model ◽

Real Data ◽

Key Performance Indicators ◽

Production Logistics ◽

Low Performance

The high complexity of today´s manufacturing environment brings many problems with planning and managing, especially production, logistic and other key business processes. In many cases, it is quite complicated to identify the real causes of problems that enterprise faces or to decide which one of them should be solved as a first. Especially, in the case of large enterprises, it is quite complicated to access expertise among all departments and employed professionals in order to solve the problems in the most efficient way. The purpose of our fuzzy model is to provide a simple tool for easy identification of the most significant problems of observed processes that causes their low performance according to the measured values of their key performance indicators (KPIs). The model is based on data gained through the interviews with production managers, industry experts and other professionals, and it was verified by real data from one model company. The results are presented in the form of case study in this contribution. Keywords: Production logistics, key performance indicators (KPI), productivity, problem identification, fuzzy set theory, process.

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Modeling human newspaper readers: The Fuzzy Believer approach

Natural Language Engineering ◽

10.1017/s1351324912000289 ◽

2012 ◽

Vol 20 (2) ◽

pp. 261-288

Author(s):

RALF KRESTEL ◽

SABINE BERGLER ◽

RENÉ WITTE

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Information Extraction ◽

Set Theory ◽

Language Processing ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Information Sources ◽

Human Agent ◽

Available Information

AbstractThe growing number of publicly available information sources makes it impossible for individuals to keep track of all the various opinions on one topic. The goal of ourFuzzy Believersystem presented in this paper is to extract and analyze statements of opinion from newspaper articles. Beliefs are modeled using the fuzzy set theory, applied after Natural Language Processing-based information extraction. The Fuzzy Believer models a human agent, deciding what statements to believe or reject based on a range of configurable strategies.

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The basic arithmetic operations on fuzzy numbers and new approaches to the theory of fuzzy numbers under the classification of space images

Mathematical machines and systems ◽

10.34121/1028-9763-2020-3-49-59 ◽

2020 ◽

Vol 3 ◽

pp. 49-59

Author(s):

S.I. Alpert ◽

Keyword(s):

Remote Sensing ◽

Fuzzy Sets ◽

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Fuzzy Numbers ◽

Mathematical Tool ◽

Classification Problems ◽

Arithmetic Operations

Classification in remote sensing is a very difficult procedure, because it involves a lot of steps and data preprocessing. Fuzzy Set Theory plays a very important role in classification problems, because the fuzzy approach can capture the structure of the image. Most concepts are fuzzy in nature. Fuzzy sets allow to deal with uncertain and imprecise data. Many classification problems are formalized by using fuzzy concepts, because crisp classes represent an oversimplification of reality, leading to wrong results of classification. Fuzzy Set Theory is an important mathematical tool to process complex and fuzzy da-ta. This theory is suitable for high resolution remote sensing image classification. Fuzzy sets and fuzzy numbers are used to determine basic probability assignment. Fuzzy numbers are used for detection of the optimal number of clusters in Fuzzy Clustering Methods. Image is modeled as a fuzzy graph, when we represent the dissimilitude between pixels in some classification tasks. Fuzzy sets are also applied in different tasks of processing digital optical images. It was noted, that fuzzy sets play an important role in analysis of results of classification, when different agreement measures between the reference data and final classification are considered. In this work arithmetic operations of fuzzy numbers using alpha-cut method were considered. Addition, subtraction, multiplication, division of fuzzy numbers and square root of fuzzy number were described in this paper. Moreover, it was illustrated examples with different arithmetic operations of fuzzy numbers. Fuzzy Set Theory and fuzzy numbers can be applied for analysis and classification of hyperspectral satellite images, solving ecological tasks, vegetation clas-sification, in remote searching for minerals.

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Present State of the Medical Expert System CADIAG-2

Methods of Information in Medicine ◽

10.1055/s-0038-1635354 ◽

1985 ◽

Vol 24 (01) ◽

pp. 13-20 ◽

Cited By ~ 47

Author(s):

K.-P. Adlassnig ◽

G. Kolarz ◽

W. Scheithauer

Keyword(s):

Expert System ◽

Clinical Diagnosis ◽

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Mathematical Tool ◽

Diagnostic Systems ◽

Medical Expert ◽

Medical Expert System ◽

Medical Diagnostic

SummaryUncertainty of knowledge about the patient and about medical relationships is generally accepted and considered to be an inherent concept in medicine. The physician, however, is quite capable of drawing conclusions from this information. Naturally, these conclusions are approximate rather than precise.Fuzzy set theory provides the possibility of defining imprecise medical entities as fuzzy sets. It offers a linguistic concept with excellent approximation to medical texts. In addition, fuzzy logic presents powerful reasoning methods that can handle approximate inferences. These facts make fuzzy set theory highly suitable for the development of computer-based medical diagnostic systems.The medical expert system CADIAG-2 provides evidence that fuzzy set theory is a suitable mathematical tool for formalizing medical processes.CADIAG-2/RHEUMA is being extensively tested on cases from a rheumatological hospital. Results from 327 cases are presented. In 265 cases, i.e. 81%, the clinical diagnosis could be either confirmed (223 cases, i.e. 68.2%) or established as a diagnostic hypothesis (42 cases, i.e. 12.8%).CADIAG-2/PANCREAS was tested on 47 cases of pancreatic diseases. In 43 cases, i.e. 91.5%, the clinical diagnosis was either confirmed by CADIAG-2 or established as one of the hypotheses with the highest or second highest number of points in a ranked list of hypotheses.

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