JMcDM

A package for Multiple-criteria decision making techniques in Julia

Installation

Please type

julia> ]
(@v1.5) pkg> add JMcDM

or

julia> using Pkg
julia> Pkg.add("JMcDM")

in Julia REPL.

Documentation

Please check out the reference manual here.

Implemented methods

MCDM Tools

  • TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions)
  • ELECTRE (Elemination and Choice Translating Reality)
  • DEMATEL (The Decision Making Trial and Evaluation Laboratory)
  • MOORA (Multi-Objective Optimization By Ratio Analysis)
  • VIKOR (VlseKriterijumska Optimizcija I Kaompromisno Resenje in Serbian)
  • AHP (Analytic Hierarchy Process)
  • DEA (Data Envelopment Analysis)
  • GRA (Grey Relational Analysis)
  • Non-dominated Sorting
  • SAW (Simple Additive Weighting) (aka WSM)
  • ARAS (Additive Ratio Assessment)
  • WPM (Weighted Product Model)
  • WASPAS (Weighted Aggregated Sum Product ASsessment)
  • EDAS (Evaluation based on Distance from Average Solution)
  • MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution)
  • MABAC (Multi-Attributive Border Approximation area Comparison)
  • MAIRCA (Multi Attributive Ideal-Real Comparative Analysis)
  • COPRAS (COmplex PRoportional ASsessment)
  • PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations)
  • CoCoSo (Combined Compromise Solution)
  • CRITIC (CRiteria Importance Through Intercriteria Correlation)
  • Entropy
  • CODAS (COmbinative Distance-based ASsessment)

SCDM Tools

  • minimax
  • maximin
  • minimin
  • maximax
  • Savage
  • Hurwicz
  • MLE
  • Laplace
  • Expected Regret

Game

  • Game solver for zero sum games

Unimplemented methods

  • UTA

  • MAUT

  • STEM

  • PAPRIKA

  • ANP (Analytical Network Process)

  • Goal Programming

  • MACBETH

  • COMET

  • will be updated soon.

Example

julia> using JMcDM, DataFrames
julia> df = DataFrame(
:age        => [6.0, 4, 12],
:size       => [140.0, 90, 140],
:price      => [150000.0, 100000, 75000],
:distance   => [950.0, 1500, 550],
:population => [1500.0, 2000, 1100]);

julia> df
3×5 DataFrame
 Row │ age      size     price     distance  population 
     │ Float64  Float64  Float64   Float64   Float64    
─────┼──────────────────────────────────────────────────
   1 │     6.0    140.0  150000.0     950.0      1500.0
   2 │     4.0     90.0  100000.0    1500.0      2000.0
   3 │    12.0    140.0   75000.0     550.0      1100.0

julia> w  = [0.35, 0.15, 0.25, 0.20, 0.05];
julia> fns = makeminmax([minimum, maximum, minimum, minimum, maximum]);
julia> result = topsis(df, w, fns);
julia> result.scores
3-element Array{Float64,1}:
0.5854753145549456
0.6517997936899308
0.41850223305822903

julia> result.bestIndex
2

Community guidelines

Do you want to contribute?

  • Please create an Issue first. In this issue, please specify the idea.
  • Let the community discuss the new contribution.

If the community decision is yes, please

  • Fork the repository
  • Send a pull request.

Please read the issue Welcome to newcomers! for implementation details.

Welcome!