Steps in Topsis:

  1. Construct the Decision Matrix: Identify the alternatives and criteria, and construct a matrix with alternatives as rows and criteria as columns.
  2. Normalize the Decision Matrix: Normalize the values to make them dimensionless and comparable.
  3. Construct the Weighted Normalized Decision Matrix: Assign weights to each criterion based on their importance and construct the weighted normalized matrix.
  4. Determine the Positive and Negative Ideal Solutions: Identify the best (positive ideal) and worst (negative ideal) values for each criterion.
  5. Calculate the Separation Measures: Compute the Euclidean distance of each alternative from the positive and negative ideal solutions.
  6. 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.


–  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.