In today's complex world, inundated with countless choices, making informed decisions is more critical than ever. This project introduces a multi-criteria decision analysis tool powered by Python and Streamlit, harnessing the potential of the (fuzzy) TOPSIS method.
TOPSIS, or the Technique for Order of Preference by Similarity to Ideal Solution, stands out as a prime method for multi-criteria decision analysis. It is designed to pinpoint the optimal solution among a range of alternatives, keeping multiple criteria or attributes in mind. The core philosophy of TOPSIS lies in spotting solutions that inch closest to the ideal solution while distancing themselves from the anti-ideal or negative-ideal solution.
Fuzzy TOPSIS is an extension of the traditional TOPSIS method, incorporating fuzzy logic to handle uncertainty and imprecision in the decision-making process. This approach is particularly useful in situations where decision criteria are not clearly defined or are subject to human judgment and perception, which is often the case in complex decision-making scenarios.
- Built with 🐍Python.
- Web interface powered by Streamlit.
- Implements the TOPSIS and fuzzy TOPSIS methods for decision making.
To get started, first clone the repository to your local machine:
git clone https://github.com/LuxF3rre/mcdm
Change your directory to the cloned repository:
cd mcdm
Install all necessary dependencies using the following command:
pip install -r requirements.txt
Launch the Streamlit app using:
streamlit run ./src/Home.py
Upon successful execution, Streamlit will provide a local URL (http://localhost:8501/). Simply open this URL in your preferred browser and follow the on-screen instructions to use the app.
For a deeper dive into the methodology and applications of TOPSIS:
- Hwang, C.L.; Lai, Y.J.; Liu, T.Y. (1993). "A new approach for multiple objective decision making". Computers and Operational Research. 20 (8): 889-899. doi:10.1016/0305-0548(93)90109-v
- El Alaoui, M. (2021). "Fuzzy TOPSIS: Logic, Approaches, and Case Studies". New York: CRC Press. doi:10.1201/9781003168416. ISBN 978-0-367-76748-8. S2CID 233525185.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
MIT License