Find the most common questions and answers about Fuzzy VIKOR, including its main steps, interpretation, and applications — and learn how to perform your own analysis easily using our online Fuzzy VIKOR software.
Basic Concepts
What is Fuzzy VIKOR?
Fuzzy VIKOR is a decision-making method that combines the VIKOR approach with fuzzy logic to handle uncertainty in human judgments when ranking alternatives.
What does VIKOR stand for?
VIKOR comes from a Serbian phrase meaning “multi-criteria optimization and compromise solution.”
What is the main goal of VIKOR?
To identify the best compromise solution among alternatives, balancing between group utility and individual regret.
Why use fuzzy logic in VIKOR?
Because human evaluations are often vague or linguistic (e.g., “good,” “poor,” “very high”), fuzzy logic helps model this uncertainty.
Who developed the VIKOR method?
VIKOR was developed by Serafim Opricovic in the late 1990s.
Methodology and Steps
What are the main steps of the Fuzzy VIKOR method?
- Define criteria and alternatives
- Convert linguistic judgments into fuzzy numbers
- Determine fuzzy best and worst values
- Compute fuzzy utility and regret measures
- Defuzzify and calculate the VIKOR index (Q)
- Rank the alternatives
What are fuzzy numbers in Fuzzy VIKOR?
They represent uncertain values (often triangular fuzzy numbers) used instead of exact numerical data.
How are fuzzy numbers defuzzified?
By using methods such as the centroid or mean-of-maxima approach.
What are S, R, and Q in VIKOR?
S: measure of group utility (overall satisfaction)
R: measure of individual regret (worst performance)
Q: compromise ranking index
How is the best alternative selected?
The alternative with the lowest Q value (compromise solution) is chosen as the best option.
Interpretation and Results
What does the compromise solution mean in VIKOR?
It represents a balanced decision that provides the closest satisfaction to the ideal solution for all criteria.
How do we interpret the Q values?
Lower Q indicates a better compromise solution; higher Q means less preferable alternatives.
What happens if two alternatives have similar Q values?
A tie-breaking rule or additional sensitivity analysis can be applied.
What is the role of the weight (v) in VIKOR?
The weight v (usually 0.5) controls the balance between group utility and individual regret.
Can we adjust the value of v?
Yes, decision-makers can test different v values (e.g., 0.25, 0.75) to analyze sensitivity.
Applications
Where is Fuzzy VIKOR used?
In supplier selection, risk assessment, sustainability evaluation, policy making, and technology selection.
Why is Fuzzy VIKOR popular in decision analysis?
Because it combines precision, flexibility, and the ability to handle uncertain or subjective data.
Can Fuzzy VIKOR handle qualitative data?
Yes, it translates qualitative expert opinions into quantitative fuzzy numbers.
Is Fuzzy VIKOR suitable for group decision-making?
Yes, it can aggregate multiple experts’ fuzzy evaluations into a single result.
Can Fuzzy VIKOR be used with other MCDM methods?
Yes, it is often combined with AHP, DEMATEL, or TOPSIS to form hybrid decision models.
Using the Software
Can I use Fuzzy VIKOR online?
Yes, you can use the Fuzzy VIKOR online software at OnlineOutput.com.
What input data is required for the software?
A list of criteria, weights, and fuzzy performance values for each alternative.
What outputs does the software provide?
Fuzzy best and worst values
Defuzzified S, R, and Q results
Ranking of alternatives
Does the software perform defuzzification automatically?
Yes, the system converts fuzzy data into crisp results automatically.
Can multiple decision-makers use the software together?
Yes, fuzzy evaluations from several experts can be averaged for a group decision.
Advanced Topics
How is Fuzzy VIKOR different from Fuzzy TOPSIS?
While both use fuzzy logic, Fuzzy VIKOR focuses on finding a compromise solution, not just the closest to the ideal point.
How does the compromise parameter (v) affect the ranking?
Changing v shifts the balance between collective satisfaction and individual regret, possibly altering the ranking.
Can sensitivity analysis be performed in Fuzzy VIKOR?
Yes, adjusting criteria weights or fuzzy ratings helps test the robustness of results.
Can Fuzzy VIKOR handle missing data?
Partially, by using expert estimation or normalization techniques.
Why is Fuzzy VIKOR important in uncertain environments?
It provides realistic, balanced, and flexible decision support when precise data is not available.
Experience the Fuzzy Vikor online software: