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
| Alternative | Criterion 1 | Criterion 2 | Criterion 3 |
|---|---|---|---|
| A1 | 50 | 60 | 70 |
| A2 | 60 | 80 | 90 |
| A3 | 70 | 85 | 80 |
Output File Example: <RollNumber>-result.csv
| Alternative | Criterion 1 | Criterion 2 | Criterion 3 | TOPSIS Score | Rank |
|---|---|---|---|---|---|
| A1 | 50 | 60 | 70 | 0.556 | 2 |
| A2 | 60 | 80 | 90 | 0.890 | 1 |
| A3 | 70 | 85 | 80 | 0.467 | 3 |
4. Usage Instructions
Run the program from the command line with the following format:
python <RollNumber>.py <InputDataFile> <Weights> <Impacts> <ResultFileName>
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Release files for 102383077-Shresth-Topsis 1.0.0
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