TOPSIS Implementation

1. Methodology

• Step 1: Data Collection
• Step 2: Data Pre-Processing (Normalization and Validation)
• Step 3: Weighted Normalization
• Step 4: Calculation of Ideal Best and Ideal Worst Solutions
• Step 5: Calculation of Separation Measures
• Step 6: Calculation of Relative Closeness (TOPSIS Score)
• Step 7: Ranking

2. Description

• Objective: To implement the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for multi-criteria decision analysis.
• Inputs:
  • A CSV file containing alternatives and their performance across criteria.
  • Weights for criteria and their respective impacts (benefit or cost).
• Outputs:
  • A CSV file with TOPSIS scores and ranks for the alternatives.
• Error Handling:
  • File existence and format validation.
  • Ensures impacts are '+' (benefit) or '-' (cost).
  • Ensures the number of weights and impacts matches the criteria count.

3. Input / Output

Input File Example: <RollNumber>-data.csv

Output File Example: <RollNumber>-result.csv

4. Usage Instructions

Run the program from the command line with the following format:

python <RollNumber>.py <InputDataFile> <Weights> <Impacts> <ResultFileName>