vaibhav-tandon-102203877 1.0.0

A Python package for implementing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method in Multi-Criteria Decision Making (MCDM)

TOPSIS Analysis Tool

This project implements the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a method for multi-criteria decision-making. The tool processes a decision matrix, applies specified weights and impacts for each criterion, and ranks the alternatives based on their performance. This Python package, developed under the acronym topsis_Vaibhav_102203877, was created as a University Student Project.

Features

• Supports input of decision matrices in CSV or Excel formats (.xlsx, .xls).
• Allows customization of weights and impacts for each criterion.
• Generates ranked results as a CSV file.

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Installation

1. Clone the repository or download the script.
2. Ensure Python (version 3.6 or later) is installed.
3. Install the required dependencies using pip:

pip install numpy pandas openpyxl

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Usage

The script uses command-line arguments to specify the input file, weights, impacts, and output file.

Command

topsis <input_file> <weights> <impacts> <output_file>

Parameters

• <input_file>: Path to the decision matrix file. Accepts .csv, .xlsx, or .xls formats.
• <weights>: Comma-separated weights for each criterion (e.g., 0.3,0.4,0.3).
• <impacts>: Comma-separated impacts for each criterion (+ for beneficial, - for non-beneficial, e.g., +,+,-).
• <output_file>: Path to save the output CSV file with rankings.

Example

Input File (data.csv):

Alternatives	Criterion 1	Criterion 2	Criterion 3
A1	250	16	12
A2	200	20	10
A3	300	18	15

Command:

topsis data.csv 0.3,0.4,0.3 +,+,- output.csv

Output File (output.csv):

Alternatives	Criterion 1	Criterion 2	Criterion 3	Rank
A1	250	16	12	2
A2	200	20	10	3
A3	300	18	15	1

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Functions

topsis(data, weights, impacts)

Executes the TOPSIS algorithm and returns the rankings of the alternatives.

Parameters:

• data: A 2D list or numpy array representing the decision matrix.
• weights: A list of weights for each criterion.
• impacts: A list of impacts (+ or -) for each criterion.

Returns:

• A list of ranks for the alternatives.

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excel_to_csv(excel_file, csv_output)

Converts an Excel file into a CSV file.

Parameters:

• excel_file: Path to the Excel file.
• csv_output: Path to save the converted CSV file.

Returns:

• The path to the converted CSV file.

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Notes

• The number of weights and impacts must match the number of criteria in the decision matrix.
• The decision matrix should not include alternative names in the criteria columns (e.g., the first column should list alternatives like A1, A2, etc.).

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License

This project is licensed under the MIT License.

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Let me know if you need further adjustments!