Mupltiple Critiera Decision Making (MCDM) using TOPSIS

Background

Program Run below Steps :

1. Convert categorical to numeric
2. Vector Normalization 2.1. Calculate Root of Sum of Squares 2.2. Find Normalized Decision Metrix - Divide every column value its Root of Sum of Squares.Value in every cell is known as Normalized performance value
3. Weight Assignment ( Calculate Weight × Normalized performance value )
4. Find Ideal Best and Ideal Worst
5. Calculate Euclidean distance from ideal best value and ideal worst value
6. Calculate Performance Score and Rank

Input -

• Input file must contain three or more columns
• First column is the object/variable name (e.g. M1, M2, M3, M4…...)
• From 2nd to last columns contain numeric values only

Output -

• Result file contains all the columns of input file and two additional columns having Topsis Score and Rank

Program check for

• Correct number of parameters (inputFileName, Weights, Impacts, resultFileName).
• Show the appropriate message for wrong inputs.
• Handling of “File not Found” exception
• Input file must contain three or more columns.
• From 2nd to last columns must contain numeric values only (Handling of non-numeric values)
• Number of weights, number of impacts and number of columns (from 2nd to last columns) must be same.
• Impacts must be either +ve or -ve.
• Impacts and weights must be separated by ‘,’ (comma).

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Installation

Use the package manager pip to install Topsis-Garima-102017070.

pip install Topsis-Arshpreet-102017045

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Usage

Enter csv filename followed by .csv extension, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-) and the csv file where result is to be stored.

Topsis 102017045-data.csv 1,1,2,1,1 +,+,+,-,+ 102017045-result.csv

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License © 2023 Arshpreet Singh

This repository is licensed under the MIT license. See LICENSE for details.