topsis-tavish-102303246 0.1.1

A Python package for Topsis method implementation

TOPSIS-Tavish-102303246

This Python-based package provides an implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). It is designed as a command-line application that evaluates multiple options against several criteria, incorporates user-defined weights and impacts, and produces a relative performance score along with a final ranking for each option.

Click Here for a live demo.

Installation

Install the package using pip as shown below:

pip install topsis-tavish-102303246

How to Use

The tool is operated from the terminal. To run it, you need an input dataset (CSV or Excel format), a list of criterion weights, a list of impacts, and an output filename where results will be stored.

Command Format

topsis <InputFile> <Weights> <Impacts> <OutputFile>

Parameter Details

1. InputFile

   • Path to a .csv or .xlsx file.
   • The dataset must include at least three columns.
   • The first column should contain identifiers for the alternatives (e.g., A1, A2, A3). This column is excluded from calculations but retained in the output.
   • All remaining columns must contain numerical values representing evaluation criteria.

2. Weights

   • A comma-separated list of numerical values representing the relative importance of each criterion (example: "2,1,3,1").

3. Impacts

   • A comma-separated list using + or - symbols to indicate whether higher or lower values are preferred.
     • + → Higher values are desirable
     • - → Lower values are desirable

4. OutputFile

   • Name of the CSV file where the computed TOPSIS scores and rankings will be saved.

Demonstration Example

Assume we want to compare four laptop models using four evaluation parameters: Cost, Battery Life, Performance, and Weight.

1. Sample Input (laptops.csv)

Laptop	Cost	Battery	Performance	Weight
L1	700	8	7	2.2
L2	650	6	6	2.5
L3	800	9	9	2.0
L4	720	7	8	2.3

Decision Logic:

• Cost: Lower is preferable (-)
• Battery Life: Higher is preferable (+)
• Performance: Higher is preferable (+)
• Weight: Lower is preferable (-)

2. Running the Tool

Execute the following command:

topsis laptops.csv "1,1,2,1" "-,+,+,-" output.csv

• Weights: Performance is given more importance than the other criteria.
• Impacts: Cost and Weight are treated as minimizing factors.

3. Generated Output (output.csv)

The resulting file includes the original dataset along with two additional fields: TOPSIS Score and Rank.

Laptop	Cost	Battery	Performance	Weight	TOPSIS Score	Rank
L1	700	8	7	2.2	0.521	3
L2	650	6	6	2.5	0.312	4
L3	800	9	9	2.0	0.742	1
L4	720	7	8	2.3	0.603	2