topsis-gurkirat-102303256 1.1.1

A professional command-line tool for TOPSIS decision analysis.

TOPSIS Implementation

BY: Gurkirat Singh
Roll Number: 102303256
Group: 3C21

topsis-gurkirat-102303256 is a Python library for solving Multiple Criteria Decision Making (MCDM) problems using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

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Installation

Install the package from PyPI using pip:

pip install topsis-gurkirat-102303256

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Usage

Run the package via the command line:

topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

Example

topsis topsis_dataset.csv "0.2,0.15,0.15,0.2,0.15,0.15" "-,+,+,+,+,+" result.csv

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Steps Followed in TOPSIS

1. Normalization
   Transform the data into a comparable scale.

2. Weighting
   Apply user-defined weights to the normalized decision matrix.

3. Ideal Solutions
   Determine the ideal best and ideal worst values for each criterion.

4. Distance Calculation
   Compute the Euclidean distance of each alternative from the ideal best and ideal worst.

5. Closeness Score
   Calculate the relative closeness of each alternative to the ideal solution.

6. Ranking
   Rank the alternatives based on the TOPSIS score.

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Sample Dataset

Alternative	Cost	Quality	Delivery Time	Durability	Service	Energy Efficiency
A1	250	7	5	8	8	6
A2	200	6	7	7	7	7
A3	300	8	4	9	9	8
A4	275	7	6	8	8	7
A5	225	9	3	9	8	9
A6	260	8	5	8	9	8

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Output (Printed in Terminal)

--- TOPSIS RESULTS (Score & Rank) ---
   Topsis Score    Rank
       0.430121     5
       0.651113     1
       0.359169     6
       0.517720     3
       0.447729     4
       0.537590     2
-------------------------------------
Results saved to result.csv

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Description

This package helps users evaluate and rank alternatives based on multiple criteria by applying the TOPSIS methodology.
It is suitable for academic assignments, research work, and real-world decision-making problems.

Before proceeding, make sure of the following points:

1. Correct number of parameters (inputFileName, Weights, Impacts, resultFileName).
2. The input file must contain three or more columns.
3. From the 2nd to the last columns, all values must be numeric.
4. The number of weights, number of impacts, and number of criteria columns (from 2nd to last) must be the same.

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License

MIT