🤔 mcdm

mcdm is a Rust library to assist with solving Multiple-Criteria Decision Making (MCDM) problems. It provides a comprehensive set of tools for decision analysis, including normalization techniques, weighting methods, and ranking algorithms.

✨ Features

MCDM involves evaluating multiple conflicting criteria when making decisions. This library aims to provide various techniques to normalize data, assign weights to criteria, and rank alternatives. Below is a list of the components that have been implemented.

🔄 Normalization

Normalization ensures that criteria with different units of measurement can be compared on a common scale. Each method has a specific use case depending on the decision problem.

⚖️ Weighting

Weights reflect the relative importance of each criterion. Different weighting techniques help balance criteria appropriately in the decision-making process.

📈 Ranking Methods

Ranking algorithms combine normalized data and weights to determine the best alternative. These methods aim to provide a clear ranking of alternatives based on the decision-maker's preferences.

🚀 Usage

To start using mcdm, add this to your Cargo.toml:

[dependencies]
mcdm = "0.1"

🧪 Example

Here’s an example demonstrating how to use the mcdm library for decision-making with the TOPSIS method:

use mcdm::{
    errors::McdmError,
    rankings::{Rank, TOPSIS},
    normalization::{MinMax, Normalize},
    weights::{Equal, Weight},
    CriteriaType,
};
use ndarray::array;

fn main() -> Result<(), McdmError> {
    // Define the decision matrix (alternatives x criteria)
    let alternatives = array![[4.0, 7.0, 8.0], [2.0, 9.0, 6.0], [3.0, 6.0, 9.0]];
    let criteria_types = CriteriaType::from_vec(vec![-1, 1, 1])?;
    // Apply normalization using Min-Max
    let normalized_matrix = MinMax::normalize(&alternatives, &criteria_types)?;
    // Alternatively, use equal weights
    let equal_weights = Equal::weight(&normalized_matrix)?;
    // Apply the TOPSIS method for ranking
    let ranking = TOPSIS::rank(&normalized_matrix, &equal_weights)?;
    // Output the ranking
    println!("Ranking: {:.3?}", ranking);
    Ok(())
}

🤝 Contributing

Contributing is what makes the open source community thrive. Any contributions to enhance mcdm are welcome and greatly apprecaited!

If you have any suggestions that could make this project better, please feel free to fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork this repo
2. Create your feature branch (git checkout -b feature/new-decision-method)
3. Commit your changes (git commit -m 'Add new decision method')
4. Push to the remote branch (git push origin feature/new-decision-method)
5. Open a pull request

📄 License

Distributed under the MIT license. See LICENSE-MIT for more information.