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.

The Bayesian Compromise Decision Support Problem for Multilevel Design Involving Uncertainty

1994 ◽  
Vol 116 (2) ◽  
pp. 388-395 ◽  
Author(s):  
S. Vadde ◽  
J. K. Allen ◽  
F. Mistree
Keyword(s):  
Decision Support ◽  
Bayesian Statistics ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Frame Structure ◽  
Bayesian Decision ◽  
Model Uncertainties ◽  
Research Issues ◽  
Portal Frame ◽  
Portal Frame Structure

In this paper we present an extension to the traditional compromise Decision Support Problem (DSP) formulation. In this formulation we use Bayesian statistics to model uncertainties associated with the information being used. In an earlier paper we have introduced a compromise DSP that accounts for uncertainty using fuzzy set theory. In this paper we describe the Bayesian Decision Support Problem. We use this formulation to design a portal frame structure. We discuss the results and compare them with those obtained using the fuzzy DSP. Finally, we discuss the efficacy of incorporating Bayesian statistics into the traditional compromise DSP formulation and describe some of the pending research issues.

Test of a Fuzzy Set Based Decision Model as an AID in Solving Human Factors Design Problems

1981 ◽  
Vol 25 (1) ◽  
pp. 306-310
Author(s):  
Richard A. Newman
Keyword(s):  
Decision Making ◽  
Human Factors ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Decision Aid ◽  
Decision Model ◽  
Decision Rules ◽  
Design Problems ◽  
Weighting Rule

Fuzzy Set Theory has proved popular for development of decision making models. However, most such models have not been tested using problems such as commonly found in Human Factors system design. This study used a decision model that combined Fuzzy Set decision rules with an eigenvector weighting rule. Five experienced Human Factors Designers solved six design problems, half manually, and half using a computer program that served as a decision making aid, using the model. On completion of the procedure, the computer model made a recommendation for a solution. The user could accept or reject the model's choice. Comparisons were made between manual and computer aided decision making, and the Fuzzy Set decision rule was compared with other possible decision rules using the same data. Results showed that use of the model-based decision aid was accepted by the users, and were reasonable. In addition, a possible measure of decision making quality was found in the measure of weighting inconsistency which is part of the eigenvector procedure.

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.

The Bayesian Compromise Decision Support Problem for Hierarchical Design Involving Uncertainty

1991 ◽  
Author(s):  
S. Vadde ◽  
R. S. Krishnamachari ◽  
F. Mistree ◽  
J. K. Allen
Keyword(s):  
Decision Support ◽  
Bayesian Statistics ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Frame Structure ◽  
Hierarchical Design ◽  
Bayesian Decision ◽  
Model Uncertainties ◽  
Research Issues ◽  
Portal Frame Structure

Abstract In this paper we present an extension to the traditional compromise Decision Support Problem (DSP) formulation. In this formulation we use Bayesian Statistics to model uncertainties associated with the information being used. In an earlier paper we have introduced a compromise DSP that accounts for uncertainty using fuzzy set theory. In this paper we describe the Bayesian Decision Support Problem. We use this formulation to design a portal frame structure. We discuss the results and compare them with those obtained using the Fuzzy DSP. Finally, we discuss the efficacy of incorporating Bayesian Statistics into the traditional compromise DSP formulation and describe some of the pending research issues.

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.

scholarly journals Trends in Fuzzy Statistics

2016 ◽  
Vol 32 (3) ◽  
pp. 239-257 ◽  
Author(s):  
S. Mahmoud Taheri
Keyword(s):  
Bayesian Statistics ◽  
Statistical Methods ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Regression ◽  
Hypotheses Testing ◽  
Fuzzy Statistics ◽  
Fuzzy Hypotheses

After introducing and developing fuzzy set theory, a lot of studies have been done to combine statistical methods and fuzzy set theory. Thisworks, called fuzzy statistics, have been developed in some branches.In this article we review essential works on fuzzy estimation, fuzzy hypotheses testing, fuzzy regression, fuzzy Bayesian statistics, and some relevant fields.

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