102203162-topsis 0.1.0

A Python library for the TOPSIS decision-making method.

TOPSIS Library

Overview

The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) library provides an implementation of the TOPSIS decision-making method. This technique is used to rank alternatives based on multiple criteria by comparing their distance from an ideal best and an ideal worst solution.

Features

• Normalize decision matrices
• Apply weights and impacts to criteria
• Calculate rankings and performance scores

Installation

Install the library via pip:

pip install topsis-library

Usage

Here’s a quick guide to using the library:

from topsis.topsis import topsis

# Define the decision matrix
# Each row is an alternative, and each column is a criterion
data = [
    [250, 16, 12, 5],
    [200, 16, 8, 3],
    [300, 32, 16, 4],
    [275, 32, 8, 4],
    [225, 16, 16, 2]
]

# Define weights for each criterion
weights = [0.25, 0.25, 0.25, 0.25]

# Define impacts for each criterion ('+' for benefit, '-' for cost)
impacts = ['+', '+', '-', '+']

# Calculate rankings and scores
rankings, scores = topsis(data, weights, impacts)

print("Rankings:", rankings)
print("Scores:", scores)

Output Example

Rankings: [3, 1, 4, 2, 5]
Scores: [0.7722, 0.5634, 0.8523, 0.6472, 0.4321]

How It Works

1. Normalization: The decision matrix is normalized to make criteria comparable.
2. Weight Application: Each criterion is weighted to reflect its importance.
3. Ideal Solutions: The algorithm calculates the ideal best and worst values for each criterion.
4. Distance Calculation: Distances from the ideal best and worst solutions are computed.
5. Performance Scores: Scores are calculated based on proximity to the ideal solutions.
6. Ranking: Alternatives are ranked based on their scores.

Testing

Run unit tests to ensure functionality:

python -m unittest discover tests

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests on GitHub.

Author

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