TOPSIS

Implementing TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) algorithm with Python and NumPy.

Context:

TOPSIS is a multi-criteria decision-making method used to determine the best alternative from a set of options/objects by considering their proximity to an ideal solution and their distance from negative ideal solutions in a multi-dimensional space.

• More information about the method can be found at: TOPSIS - Um Algoritmo de Tomada de Decisão

Dependencies:

All necessary dependencies are included in requirements.txt. To install all of them, perform the following command in your terminal:

pip install -r requirements.txt

How to Run:

After installing all dependecies, beware to be inside directory topsis/src to run main.py file to avoid errors in the present example.

To run personal examples, make sure to change input/data.csv dataset and to correctly attribute criterias ("C" or "B" for Cost or Benefit) and weights in input/data.json, accordingly to the attributes in your dataset's columns.

Results:

In this example, I performed TOPSIS to determine which object Supplier would be better based on the following attributes: "Cost", "Quality", "Delivery Time", "Reliability" and "Environmental Impact".

Here are the results:

• output/rank.png:

• output/rank.txt:

Ranking:
1. Supplier D -> Score: 0.6339
2. Supplier B -> Score: 0.6167
3. Supplier A -> Score: 0.5140
4. Supplier C -> Score: 0.2485