scholarly journals Fuzzy Linear Physical Programming for Multiple Criteria Decision-Making Under Uncertainty

Author(s):  
Ahmed ElSayed ◽  
Elif Kongar ◽  
Surendra M. Gupta

<p>This paper presents a newly developed fuzzy linear physical programming (FLPP) model that allows the decision maker to introduce his/her preferences for multiple criteria decision making in a fuzzy environment. The major contribution of this research is to generalize the current models by accommodating an environment that is conducive to fuzzy problem solving. An example is used to evaluate, compare and discuss the results of the proposed model.</p>

Author(s):  
G G Davidson ◽  
A W Labib

This paper proposes a new concept of decision analysis based on a multiple criteria decision making (MCDM) process. This is achieved through the provision of a systematic and generic methodology for the implementation of design improvements based on experience of past failures. This is illustrated in the form of a case study identifying the changes made to Concorde after the 2000 accident. The proposed model uses the analytic hierarchy process (AHP) mathematical model as a backbone and integrates elements of a modified failure modes and effects analysis (FMEA). The AHP has proven to be an invaluable tool for decision support since it allows a fully documented and transparent decision to be made with full accountability. In addition, it facilitates the task of justifying improvement decisions. The paper is divided as follows: the first section presents an outline of the background to the Concorde accident and its history of related (non-catastrophic) malfunctions. The AHP methodology and its mathematical representation are then presented with the integrated FMEA applied to the Concorde accident. The case study arrives at the same conclusion as engineers working on Concorde after the accident: that the aircraft may fly again if the lining of the fuel tanks are modified.


2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Sen-Kuei Liao ◽  
Hsiao-Yin Hsu ◽  
Kuei-Lun Chang

Location selection is a critical problem for businesses that can determine the success of an organization. Selecting the optimal location from a pool of alternatives belongs to a multiple criteria decision making (MCDM) problem. This study employed a hybrid MCDM technique to select locations for women’s fitness centers in Taiwan. In the beginning, the fuzzy Delphi method was utilized to obtain selection criteria from interviewed senior executives. In the second stage, the decision making trial and evaluation laboratory (DEMATEL) was employed to extract interdependencies between the selection criteria within each perspective. On the basis of interdependencies between the selection criteria, the analytic network process (ANP) was used to get respective weights of each criterion. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) was ranking the alternatives. To demonstrate application of the proposed model and illustrate a location selection problem, a case was conducted. The capabilities and effectiveness of the proposed model are revealed.


2016 ◽  
Vol 15 (05) ◽  
pp. 1157-1179 ◽  
Author(s):  
N. Thillaigovindan ◽  
S. Anita Shanthi ◽  
J. Vadivel Naidu

This paper considers a multiple criteria decision-making (MCDM) problem under risk in fuzzy environment in its general form. There are m alternatives which need to be ranked on the basis of a set of n criteria. The alternatives and the criteria are evaluated based on a set of l characteristics. The entire data is presented in the form of interval valued intuitionistic fuzzy soft set of root type. In addition each criterion is assigned a subjective criterion weight based on expert’s evaluation and each characteristic is assigned a probability weight on the basis of decision maker’s knowlege and understanding of the importance of the characteristic. This problem may be called as a MCDM problem under risk in fuzzy environment in its general form. A method for ranking the alternatives using the new score functions, prospect theory and method of determining the optimum criteria weights is explained. An algorithm is developed for this purpose and its working illustrated with a suitable example.


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