Aggregation of fuzzy opinions under group decision making
Fuzzy Sets and Systems
Group decision making procedure considering preference strength under incomplete information
Computers and Operations Research
Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
A fuzzy approach to select the location of the distribution center
Fuzzy Sets and Systems
Fuzzy linear programming technique for multiattribute group decision making in fuzzy environments
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An extension of TOPSIS for group decision making
Mathematical and Computer Modelling: An International Journal
Approximate TOPSIS for vessel selection under uncertain environment
Expert Systems with Applications: An International Journal
| Hi-index | 12.10 |
A fuzzy Multi Attribute Decision Making (FMADM) method, which is suitable for treating group decision making problems in a fuzzy environment, is proposed for ranking offshore well barriers from a cost-benefit view point. It is obvious that much knowledge in the real world is fuzzy rather than precise. MADM decision data is usually fuzzy, crisp, or a combination of the two. A useful model is proposed here in order to handle both fuzzy and crisp data. Imprecision and ambiguity in the calculation of a performance rating are incorporated into MADM whereby fuzzy set theory provides a mathematical framework for modeling them. Human opinions often conflict in group decision-making. The purpose of fuzzy MADM is to aggregate the conflicting opinions. In general, one expert's opinion for a given attribute may be different from others'. Therefore, it is necessary to develop an appropriate method of aggregating multiple experts' opinions, taking into account a degree of importance of each expert in the aggregation procedure. The weights of all attributes and experts are estimated using a Fuzzy Analytical Hierarchy Process (FAHP). Finally, the best well barrier or risk control option (RCO) with respect to cost and benefit is selected using a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method.