Have you ever tried to compare a few options and gotten stuck? 🤔
The choices seem logical, but you just can't decide on the best one? 😅
I get it—it takes Math to choose! 🔢✨
This is a simple implementation of well-known methods for multi-attribute decision making (MADM). Whether you're making everyday decisions or tackling larger ones (like buying a house 🏡), this repo can help you choose the best option with confidence!
Before making a final decision, you need to normalize your decision matrix to avoid mixing different metrics together. This code helps you with that:
✅ Normalizes the decision matrix using three different methods:
✅ Linear Normalization 🔄
✅ Euclidean Normalization 🧮
✅ Max-Min Normalization 📊
Once the matrix is normalized, there are several ways to calculate the best alternative. Here’s a brief overview:
This method compares alternatives by evaluating their pairwise concordance and net scores. While effective, it’s computationally expensive. We strongly advise not using more than 5 attributes with this method. If you have more than 5, consider one of the other methods.
✅ Implements the Permutation Method to calculate:
✅ Concordance matrices 📈
✅ Net scores for different permutations of alternatives 🔢
The TOPSIS method ranks alternatives based on their closeness to the ideal solution (and distance from the worst solution). It's a great choice for balanced decision-making!
✅ Implements the TOPSIS Method to rank alternatives based on proximity to the ideal solution 🏆
If you prefer a more straightforward approach, the SAW method is one of the most intuitive decision-making techniques. It scores alternatives by summing their weighted normalized values.
✅ Implements the SAW Method, which scores alternatives by summing their weighted normalized values ⚖️
For a more rigorous decision-making process, the ELECTRE I method eliminates weaker alternatives using concordance and discordance indices.
✅ Implements the ELECTRE I Method to compare alternatives based on concordance and discordance indices ⚔️
Want to save your results for analysis? We've got you covered! All results are outputted into a text file for easy access and further analysis.
✅ Outputs results into a text file for easy analysis 📑
Each of these methods follows a structured approach:
1️⃣ Normalization: Transform raw values into a comparable scale ⚖️.
2️⃣ Weighting: Assign importance levels to different criteria 🎯.
3️⃣ Calculation:
✅ SAW: Sum the weighted scores for each alternative.
✅ TOPSIS: Measure distances from the ideal and worst solutions.
✅ Permutation Method: Compare alternatives using pairwise concordance and net scores.
✅ ELECTRE I: Use concordance and discordance thresholds to eliminate weaker choices.
4️⃣ Ranking: Identify the best alternative based on the chosen method 📊.
Each method has its strengths:
✅ SAW is simple but effective
✅ TOPSIS balances ideal and worst-case scenarios
✅ Permutation Method provides a thorough comparison
✅ ELECTRE I is great for eliminating poor options
To get started, you’ll need the following:
✅ Python 🐍
✅ pandas library 📚
✅ numpy library ➗
This project includes both Python script files (.py) and Jupyter Notebook files (.ipynb). Some methods are implemented in Python scripts for standalone execution, while others are demonstrated in notebooks for better visualization and step-by-step explanation. Make sure to check the appropriate file format based on your needs.