pyDecision is a comprehensive Python library that encompasses a wide array of Multi-Criteria Decision Analysis (MCDA) methods. These powerful and versatile tools assist in making effective decisions by comparing alternatives based on multiple criteria, making it a valuable resource for researchers, analysts, and decision-makers.

Scikit-Criteria is a collection of Multiple-criteria decision analysis (MCDA) methods integrated into scientific python stack. Is Open source and commercially usable.

An open source framework for interactive multiobjective optimization methods

📈Multiple-Criteria Decision Analysis (MCDA) techniques enhanced with Deep Learning Techniques to solve a Securities Selection (Stocks) problem

An MCDA add-in for ArcMap.

The Python 3 library for Multi-Criteria Decision Analysis.

The MCDA Toolkit is a lightweight .NET tool designed for Multi-Criteria Decision Analysis (MCDA).

A web application that aims to rank resumes in relation to a job offer using Multiple Criteria Decision Aid (MCDA) to sort the CVs.

Java Evolutionary Multi-Objective Algorithm

Python 3 library for Multi-Criteria Decision Analysis based on distance metrics, providing twenty different distance metrics.

Python Package (on PyPI) for MCDM using TOPSIS Technique

Collection of generalized p-mean models with classic, fuzzy and unweighted approach

Plot Finite Partially Ordered Sets in Python

Framework for ranking prediction based on Multi-Layer Perceptron (MLP) regressor model and historical datasets evaluated by experts using Multi-Criteria Decision Analysis (MCDA) methods in Python.

multi-criteria decision analysis (MCDA) of various harms of drugs used recreationally in the UK

RMCriteria is a package to solve Multiple-Criteria Decision Analysis (MCDA) problems. For now, it only supports Promethee methods, but other methods may be developed in the future.

Decision making platform using multi-criteria decision analysis methods. Free and open source

This is Python 3 library for multi-criteria decision analysis with decision-maker preference identification based on historical datasets using evolutionary stochastic algorithm Differential evolution

Simple Best worst method implementation.