mcdm

Run Time for AIE and FPGA based platforms

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.

A package for Multiple criteria decision-making techniques in Julia

An open source computational and fullstack framework for interactive multiobjective optimization and decision-support..

Multiple-criteria decision-making (MCDM) with Electre, Promethee, Weighted Sum and Pareto

Unweighted TOPSIS method

The RMCDA package is a comprehensive toolkit for Multi-Criteria Decision Analysis (MCDA), designed to facilitate the evaluation and ranking of alternatives based on multiple criteria. The package provides methods for implementing various MCDA techniques, including pairwise comparisons, partial-order analysis, and dominance-based decision rules.

This repository contains Python implementations of various Multi-Criteria Decision Making (MCDM) methods. These techniques help in evaluating and ranking alternatives based on multiple criteria, often used in decision analysis and optimization problems.

Implementation of TOPSIS method for Multiple-Criteria Decision Making (MCDM).

Multi-Objective Optimization Repository

Unweighted VIKOR method

Academic survey tool for North Star Metric selection research — BWM + TOPSIS mixed-methods framework

Flutter package for practical multi-criteria decision-making, providing algorithms such as AHP, SAW, and TOPSIS to help developers perform weighting, evaluation, and ranking of alternatives easily and accurately within their Flutter applications.

Python Package (on PyPI) for MCDM using TOPSIS Technique

Decision Aid for Attorneys - Explainable Legal AI Assistant for Wrongful Foreclosure case triage and decision analysis. Models legal reasoning with MCDM, few-shot prompts, and court-aligned logic.

Mymcdm package is intended to serve as a tool for handling Multiple Attribute Decision Making problems. It can retrieve and process inputs, normalize alternatives, determine relative weights of criteria and process the inputs using decision methods to determine the decision objective of the model.

This repository contains a python script for using SWARA (a criteria prioritization method).

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

Run a Tesla P40 (Pascal, sm_61) under WSL2 for local LLM serving: MCDM driver model, CUDA 12.9, Docker GPU passthrough, and llama.cpp built for sm_61. Benchmarked on Qwen3.6-35B-A3B at ~51 tok/s with 128K context.

⚖️ Multi-criteria decision-making methods using Python.