TOPSIS Command-Line Tool

Project Description

The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) command-line tool implements the TOPSIS method, which is used in multi-criteria decision-making (MCDM) problems. The method ranks alternatives based on their relative closeness to the ideal solution. This package allows users to perform TOPSIS analysis on a dataset provided in a CSV format, specifying weights and impacts for each criterion.

Features

• Normalizes the data to compare different criteria on the same scale.
• Applies weights to different criteria.
• Identifies the ideal and worst solutions for each criterion based on impact direction (+ for benefit, - for cost).
• Ranks the alternatives based on their closeness to the ideal solution.

Installation

1. Clone the repository or download the script.

2. Ensure you have the necessary Python dependencies installed:

   pip install numpy pandas
   

How to Use

Command-Line Usage

The tool can be run via the command line and accepts four arguments:

1. input_file: Path to the input CSV file containing the data.
2. weights: Comma-separated list of weights for each criterion.
3. impacts: Comma-separated list of impacts (+ or -) for each criterion.
4. output_file: Path to save the output CSV file with the rankings and performance scores.

Input Data Format

The input CSV file must have the following structure:

• The first column (ID) should be an identifier for the alternatives.
• The subsequent columns should represent the criteria for each alternative.
• The number of criteria should be at least two.

Running the Script

Once the input CSV file is ready, you can run the script via the command line:

python topsis.py input_file.csv "0.4,0.3,0.3" "+,+,-" output_file.csv

• input_file.csv: Path to the input CSV file containing the data.
• "0.4,0.3,0.3": A comma-separated list of weights for each criterion.
• "+,+,-": A comma-separated list of impacts for each criterion (+ indicates a benefit, - indicates a cost).
• output_file.csv: Path to save the output CSV file with the rankings and performance scores.

Output

The output file (output_file.csv) will contain the following columns:

• ID: The alternative identifier.
• The original criteria columns.
• Score: The performance score for each alternative.
• Rank: The rank based on the performance score (1 being the best).

Example

For an input CSV file:

And the following arguments:

python topsis.py input_file.csv "0.5,0.3,0.2" "+,-,+"