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.

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 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.

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.

Evaluation and selection of resilient suppliers in fuzzy environment

2016 ◽  
Vol 23 (3) ◽  
pp. 651-673 ◽  
Author(s):  
Anoop Kumar Sahu ◽  
Saurav Datta ◽  
S.S. Mahapatra
Keyword(s):  
Decision Support ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Supplier Selection ◽  
General Strategy ◽  
Compromise Solution ◽  
Content Type ◽  
Theory Application ◽  
Selection Of

Purpose – Supply chains (SCs) have become increasingly vulnerable to catastrophic events/disruptions that may be natural or man-made. Hurricanes, tsunamis and floods are natural disasters, whereas man-made disasters may be strikes, terrorist attacks, etc. Failure at any point in the SC network has the potential to cause the entire network to fail. SCs must therefore be properly designed to survive well in the disruption scenario. The capability of successful survival (of the firm’s SC) against those adverse events/happenings is termed as resilience; and, the SC designed under resilience consideration is called a resilient SC. Effective supplier selection is considered as a key strategic consideration in SC management. It is felt that apart from considering traditional suppliers selection criterions, suppliers’ resiliency strategy must be incorporated while selecting a potential supplier which can provide best support to the firm even in the disaster/disruption scenario. The purpose of this paper is to focus aspects of evaluation and selection of resilience supplier by considering general as well as resiliency strategy, simultaneously. Design/methodology/approach – In this work, subjectivity associated with ill-defined (vague) evaluation information has been tackled through logical exploration of fuzzy numbers set theory. Application of VIKOR embedded with fuzzy mathematics has been utilized here. Sensitivity analysis has been performed to reflect the effect of decision-makers’ (DM) risk bearing attitude in selecting the best potential supplier in a resilient SC. A case empirical example has also been presented. Findings – The work attempts to focus on a decision-making procedural hierarchy towards effective supplier selection in a resilient SC. The work exhibits application potential of VIKOR method integrated with fuzzy set theory to select potential supplier based on general strategy as well as resiliency strategy. The final supplier selection score (obtained by considering general strategy) and that of obtained by analyzing resiliency strategy have been combined to get a final compromise solution. The decision-support framework thus reported here also considers DMs’ risk bearing attitude. Practical implications – The study bears significant impact to the industry managers who are trying to adapt resiliency strategy in their SC followed by potential supplier selection in the context of resilient SC. Originality/value – Exploration of VIKOR embedded with fuzzy set theory towards suppliers’ evaluation and selection by considering general and resiliency criteria both. The decision-support module(s) adapted in this paper considers DMs’ risk bearing attitude to arrive the best compromise solution.

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.

Design of an Aircraft Tire: A Study in Modeling Uncertainty

1992 ◽  
Author(s):  
S. Vadde ◽  
S. Swadi ◽  
N. Bhattacharya ◽  
F. Mistree ◽  
J. K. Allen
Keyword(s):  
Decision Support ◽  
Bayesian Statistics ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Model Uncertainty ◽  
Design Problems ◽  
Modeling Uncertainty ◽  
Imprecise Information ◽  
Uncertainty In Design

Abstract During the early stages of project initiation, the information available to a designer may be uncertain (imprecise or stochastic). In response to this need, two extensions of the crisp compromise Decision Support Problem using fuzzy set theory and Bayesian statistics are developed to model uncertainty in design problems. The fuzzy compromise DSP is used to model imprecise information and the Bayesian compromise DSP is used to model stochastic information. The design of an aircraft tire is used as an illustrative example.

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