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

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.

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

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.

A GENERALIZED APPROACH TO POSSIBILISTIC CLUSTERING ALGORITHMS

2007 ◽  
Vol 15 (supp02) ◽  
pp. 117-138 ◽  
Author(s):  
JIAN ZHOU ◽  
CHIH-CHENG HUNG
Keyword(s):  
Set Theory ◽  
Fuzzy Clustering ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Clustering Algorithms ◽  
Infinite Family ◽  
Real Data ◽  
Point Of View ◽  
New Family ◽  
Possibilistic Clustering

Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approaches, in this paper, a general approach of fuzzy clustering is initiated from a new point of view, in which the memberships are estimated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index. This new method is then used to develop a generalized approach of possibilistic clustering to obtain an infinite family of generalized possibilistic clustering algorithms. We also point out that the existing possibilistic clustering algorithms are members of this family. Following that, some specific possibilistic clustering algorithms in the new family are demonstrated by real data experiments, and the results show that these new proposed algorithms are efficient for clustering and easy for computer implementation.

Methods of indistinct multicriteria decision support in network planning

10.12737/4534 ◽  
2014 ◽  
Vol 4 (2) ◽  
pp. 0-0
Author(s):  
Сатторов ◽  
F. Sattorov
Keyword(s):  
Decision Support ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Multicriteria Optimization ◽  
Fuzzy Model ◽  
Fuzzy Reasoning ◽  
Time Parameter ◽  
Multicriteria Decision ◽  
Multicriteria Decision Support

In this paper we consider the solution of multicriteria decision support in the assessment of the time parameter of network plan under uncertainty fuzzy character. Proposed method is based on the mechanisms of fuzzy set theory and multicriteria optimization and represents a fuzzy model, as input parameters of which set of fuzzy criterion act, the calculation in a fuzzy model is carried out on the bases of fuzzy reasoning (logical implication) of the base of rules, and as an output parameter of model, ie, possibilistic duration of work acts as the resulting function.

scholarly journals A FUZZY MODEL FOR AN INCREASE IN LOCOMOTIVE TRACTION FORCE

Transport ◽  
2010 ◽  
Vol 25 (1) ◽  
pp. 36-45 ◽  
Author(s):  
Marko Djukić ◽  
Srdjan Rusov ◽  
Zoran Mitrović
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Optimization Model ◽  
Mechanical Model ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Traction Force ◽  
Numerical Examples

This paper deals with the process of traction force realization described by a suitable mechanical model and is pointed to the adhesion phenomenon as a physical one, i.e. is a suitable factor that the value of traction force depends on. The model for the process of optimizing locomotive traction force based on using the fuzzy set theory is explained. The projecting process of a fuzzy controller regulating the value of skidding and the value of traction torque by increasing the value of traction force that can be realized according to adhesion conditions is described. Finally, testing the optimization model in several numerical examples under specific conditions of wheel-rail adhesion is done.

HEDGE ALGEBRAS, LINGUISTIC-VALUE LOGIC AND THEIR APPLICATION TO FUZZY REASONING

1999 ◽  
Vol 07 (04) ◽  
pp. 347-361 ◽  
Author(s):  
CAT HO NGUYEN ◽  
DINH KHANG TRAN ◽  
HUYNH VAN NAM ◽  
HAI CHAU NGUYEN
Keyword(s):  
Fuzzy Control ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Model ◽  
Fuzzy Reasoning ◽  
Approximate Reasoning ◽  
Linguistic Terms ◽  
Natural Languages ◽  
The Given

People use natural languages to think, to reason, to deduce conclusions, and to make decisions. Fuzzy set theory introduced by L. A. Zadeh has been intensively developed and founded a computational foundation for modeling human reasoning processs. The contribution of this theory both in the theoretical and the applied aspects is well recognized. However, the traditional fuzzy set theory cannot handle linguistic terms directly. In our approach, we have constructed algebraic structures to model linguistic domains, and developed a method of linguistic reasoning, which directly manipulates linguistic terms, In particular, our approach can be applied to fuzzy control problems. In many applications of expert systems or fuzzy control, there exist numerous fuzzy reasoning methods. Intuitively, the effectiveness of each method depends on how well this method satisfies the following criterion: the similarity degree between the conclusion (the output) of the method and the consequence of an if-then sentence (in the given fuzzy model) should be the "same" as that between the input of the method and the antecedent of this if-then sentence. To formalize this idea, we introduce a "measure function" to measure the similarity between linguistic terms in a domain of any linguistic variable and to build approximate reasoning methods. The resulting comparison between our method and some other methods shows that our method is simple and more effective.

Using Case Data to Ensure ‘Real World’ Input Validation within Fuzzy Set Theory Models

2012 ◽  
pp. 61-89 ◽  
Author(s):  
Sara Denize ◽  
Sharon Purchase ◽  
Doina Olaru
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Real World ◽  
Fuzzy Set Theory ◽  
Fuzzy Model ◽  
Considerable Potential ◽  
Fuzzy Models ◽  
Case Data ◽  
Input Validation ◽  
Case Based

Fuzzy set theory models have considerable potential to address complex marketing and B2B problems, but for this methodology to be accepted, models require validation. However, there is relatively little detail in the literature dealing with validation of fuzzy simulation in marketing. This limitation is compounded by the difficulty of using case-based and qualitative evidence (data to which fuzzy models are well suited) when applying more general validation. The chapter illustrates a fuzzy model validation process using small-N cased based data and concludes with recommendations to assist researchers in validating their fuzzy models.

scholarly journals Evaluation of Students Performance using Fuzzy Set Theory in Online Learning of Islamic Finance Course

2021 ◽  
Vol 15 (07) ◽  
pp. 202
Author(s):  
Nashirah Abu Bakar ◽  
Sofian Rosbi ◽  
Azizi Abu Bakar
Keyword(s):  
Online Learning ◽  
Student Performance ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Real Data ◽  
Islamic Finance ◽  
Final Examination ◽  
Fuzzy Set Analysis ◽  
Self Learning

<p class="0abstract"><strong>Abstract—</strong>The objective of this study is to evaluate student performance using fuzzy set theory in Islamic Finance online course. This study focuses on selecting best individual among 30 students that registered for Islamic Bank Management course. The variables that involved in this study are online quiz marks, online assignment marks and online self-learning time.  The outcome of the fuzzy set analysis was compared with final examination data. The methodology of this study involving converting real data to fuzzy set, intersection calculation, decision analysis using maximizing approach. Result of fuzzy set shows the best individual score is 0.9. This student selected as best candidate for student performance in online learning with considering three variables namely online quizzes, online assignment and online self-learning hour. The comparison with final examination marks shows a good agreement with fuzzy set theory that concluded best individual from fuzzy set theory exhibits highest performance during final examination. The main finding of this study can help educators to predict the best performer in online learning class. In the same time, finding of this study can act as guideline to advise students in achieving their desired grade for online learning course.</p>

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

2016 ◽  
Vol 22 (6) ◽  
pp. 783-807
Author(s):  
Ghasem BAGHERZADEH ◽  
Kaveh M. CYRUS ◽  
Abdolreza YAZDANI-CHAMZINI ◽  
Algita MIEČINSKIENĖ
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Business Processes ◽  
Mathematical Tool ◽  
Software Environment ◽  
Effective Components ◽  
Operational Variables ◽  
Major Factors ◽  
Available Information

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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