Steps in Topsis:
- Construct the Decision Matrix: Identify the alternatives and criteria, and construct a matrix with alternatives as rows and criteria as columns.
- Normalize the Decision Matrix: Normalize the values to make them dimensionless and comparable.
- Construct the Weighted Normalized Decision Matrix: Assign weights to each criterion based on their importance and construct the weighted normalized matrix.
- Determine the Positive and Negative Ideal Solutions: Identify the best (positive ideal) and worst (negative ideal) values for each criterion.
- Calculate the Separation Measures: Compute the Euclidean distance of each alternative from the positive and negative ideal solutions.
- Calculate the Relative Closeness to the Ideal Solution: Determine the closeness of each alternative to the ideal solution and rank the alternatives based on these values.
Advantages of Topsis:
– Simplicity and Intuitive : TOPSIS is straightforward and easy to understand.
– Efficiency : It efficiently handles large datasets and multiple criteria.
– Flexibility : It can be adapted to various types of decision-making problems and different criteria weights.
Limitations:
– Subjectivity in Weight Assignment : The method’s results heavily depend on the weights assigned to the criteria, which can be subjective.
– Compensation Effect : Poor performance in one criterion can be compensated by good performance in another, which may not always be desirable.
In summary, TOPSIS is a valuable tool in decision-making processes where multiple criteria need to be considered, providing a clear and rational way to rank and select the best alternatives.