Topsis-Arjun-102017005 1.0.2

A Python package for Multiple Criteria Decision Analysis (MCDA) using TOPSIS Method made by Arjun Khanchandani.

topsis_arjun_102017005

What is TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in 1981 as a multi-criteria decision analysis method.
TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).

Installation

pip install Topsis-Arjun-102017005

How to use it?

Command Prompt

Topsis-Arjun-102017005 input.csv "1,1,1,1,1" "+,-,+,+,+" output.csv

Command Prompt

Topsis-Arjun-102017005 <python_file> <Input Data File> <Weights> <Impacts> <Result File Name>

Input csv format

Input file contain three or more columns
First column is the variable/quantity name
From 2nd to last columns contain numeric (int/float) values only

Example:

topsis topsis.py inputfile.csv “1,1,1,1,2” “+,+,+,+,-” result.csv

Note: The weights and impacts should be ',' seperated, input file should be in pwd.

Sample input data

Model	P1	P2	P3	P4	P5
M1	0.62	0.38	3.8	33.8	9.65
M2	0.75	0.56	5.7	50.3	14.33
M3	0.95	0.90	6.5	65.6	18.49
M4	0.61	0.37	6.2	43.6	12.70
M5	0.60	0.36	6.4	61.2	17.14
M6	0.76	0.58	5.3	68.0	18.66
M7	0.66	0.44	6.2	47.2	13.63
M8	0.80	0.64	5.7	37.1	11.06

Sample output data

Model	P1	P2	P3	P4	P5	Performance Score	Topsis Rank
M1	0.62	0.38	3.8	33.8	9.65	0.317272185	8
M2	0.75	0.56	5.7	50.3	14.33	0.452068871	4
M3	0.95	0.90	6.5	65.6	18.49	0.689037307	1
M4	0.61	0.37	6.2	43.6	12.70	0.340383903	7
M5	0.60	0.36	6.4	61.2	17.14	0.367206376	6
M6	0.76	0.58	5.3	68.0	18.66	0.481350901	3
M7	0.66	0.44	6.2	47.2	13.63	0.372999972	5
M8	0.80	0.64	5.7	37.1	11.06	0.51226635	2