TOPSIS

Code by: Arth Duggal

TOPSIS is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. Normalisation is usually required as the parameters or criteria are often of incongruous dimensions in multi-criteria problems. Compensatory methods such as TOPSIS allow trade-offs between criteria, where a poor result in one criterion can be negated by a good result in another criterion. This provides a more realistic form of modelling than non-compensatory methods, which include or exclude alternative solutions based on hard cut-offs. An example of application on nuclear power plants is provided in.TOPSIS stands for ‘The Technique for Order of Preference by Similarity to the Ideal Solution’ is a multi-criteria decision analysis(MCDA) method.

How to run

Before running, make sure you have pandas installed on your system

Open Terminal and input the following commands

pip install Topsis-Arth-101803214

python

from topsis.topsis1 import topsis topsis("input.csv","1,2,1,2","+,+,-,+","output.csv")

Sample Input

This input was used to test the module

Output

License

© 2020 Arth Duggal

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