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.

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.

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.

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.

BI-IDEALS OF ORDERED SEMIGROUPS BASED ON THE INTERVAL-VALUED FUZZY POINT

2016 ◽  
Vol 78 (2) ◽  
Author(s):  
Hidayat Ullah Khan ◽  
Nor Haniza Sarmin ◽  
Asghar Khan ◽  
Faiz Muhammad Khan
Keyword(s):  
Fuzzy Sets ◽  
Set Theory ◽  
Fuzzy Set ◽  
Real World ◽  
Fuzzy Set Theory ◽  
Fuzzy Subset ◽  
Ordered Semigroups ◽  
Fuzzy Point ◽  
Interval Valued ◽  
Real World Problems

Interval-valued fuzzy set theory (advanced generalization of Zadeh's fuzzy sets) is a more generalized theory that can deal with real world problems more precisely than ordinary fuzzy set theory. In this paper, we introduce the notion of generalized quasi-coincident with () relation of an interval-valued fuzzy point with an interval-valued fuzzy set. In fact, this new concept is a more generalized form of quasi-coincident with relation of an interval-valued fuzzy point with an interval-valued fuzzy set. Applying this newly defined idea, the notion of an interval-valued -fuzzy bi-ideal is introduced. Moreover, some characterizations of interval-valued -fuzzy bi-ideals are described. It is shown that an interval-valued -fuzzy bi-ideal is an interval-valued fuzzy bi-ideal by imposing a condition on interval-valued fuzzy subset. Finally, the concept of implication-based interval-valued fuzzy bi-ideals, characterizations of an interval-valued fuzzy bi-ideal and an interval-valued -fuzzy bi-ideal are considered. 

Flexible Querying Techniques Based on CBR

2011 ◽  
pp. 167-190
Author(s):  
Guy De Tré ◽  
Marysa Demoor ◽  
Bert Callens ◽  
Lise Gosseye
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Possibility Theory ◽  
Case Based Reasoning ◽  
Similarity Matching ◽  
Untreated Case ◽  
Flexible Querying ◽  
Case Based

In case-based reasoning (CBR), a new untreated case is compared to cases that have been treated earlier, after which data from the similar cases (if found) are used to predict the corresponding unknown data values for the new case. Because case comparisons will seldom result in an exact-similarity matching of cases and the conventional CBR approaches do not efficiently deal with such imperfections, more advanced approaches that adequately cope with these imperfections can help to enhance CBR. Moreover, CBR in its turn can be used to enhance flexible querying. In this chapter, we describe how fuzzy set theory can be used to model a gradation in similarity of the cases and how the inevitable uncertainty that occurs when predictions are made can be handled using possibility theory resulting in what we call flexible CBR. Furthermore, we present how and under which conditions flexible CBR can be used to enhance flexible querying of regular databases.

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