https://github.com/jbytecode/jmcdm
A package for Multiple criteria decision-making techniques in Julia
https://github.com/jbytecode/jmcdm
julia mcdm multiple-criteria-decision-making optimization
Last synced: 10 months ago
JSON representation
A package for Multiple criteria decision-making techniques in Julia
- Host: GitHub
- URL: https://github.com/jbytecode/jmcdm
- Owner: jbytecode
- License: mit
- Created: 2021-02-03T08:04:21.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2025-04-04T19:17:20.000Z (10 months ago)
- Last Synced: 2025-04-06T00:07:08.399Z (10 months ago)
- Topics: julia, mcdm, multiple-criteria-decision-making, optimization
- Language: Julia
- Homepage:
- Size: 3.21 MB
- Stars: 53
- Watchers: 4
- Forks: 9
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.txt
Awesome Lists containing this project
README
[](https://doi.org/10.21105/joss.03430)
[](https://jbytecode.github.io/JMcDM/)
[](https://codecov.io/gh/jbytecode/JMcDM)
# JMcDM
A package for Multiple-criteria decision-making techniques in Julia.
## The problem
Suppose a decision process has n alternatives and m criteria which are either to be maximized or minimized. Each single criterion has a weight _0 ≤ wᵢ ≤ 1_ where sum of _wᵢ_ is 1. _fᵢ_ is either maximum or minimum. _gⱼ(.)_ is the evolution function and it is choosen as _gⱼ(x) = x_ in many methods. A multiple criteria decision problem can be represented using the decision table
without loss of generality. When _A₁, A₂, ..., Aₙ_ are alternatives and _C₁, C₂, ..., Cₙ_ are different situations of a single criterion then the decision problem is said to be single criterion decision problem. If _Cⱼ_ are strategies of two game players then _gⱼ(Aᵢ)_ is the gain of the row player when she selects the strategy _i_ and the column player selects the strategy _Cⱼ_.
The package mainly focuses on solving these kinds of decision problems.
## For whom?
Multiple-criteria decision-making is an inter-discipline subject and there is a vast amount of research in the literature in this area. However, the existing software packages in this area are generally focused on a small subset of tools. JMcDM is a developer and researcher-friendly Julia package that combines the developed methods, utility functions for implementing new ones, and serves an environment for comparing results of multiple analyses.
## Installation
Please type
```julia
julia> ]
(@v1.10) pkg> add JMcDM
```
or
```julia
julia> using Pkg
julia> Pkg.add("JMcDM")
```
in Julia REPL to install package in the standard way, otherwise, you can clone the repository
and type
```julia
julia> ]
(@v1.10) pkg> develop .
```
to start developing new methods.
## Package Dependencies
Since the Julia package manager installs all of the dependencies automatically, a standard user doesn't need to
install them manually. The package dependencies are listed below:
- Requires.jl
for the main functionality. You can also install the
- JuMP
- Ipopt
for zero-sum game problem solving, data envelopment analysis, and the SECA method. You can also import
- DataFrames
manually for user-friendly interactions. All of the methods require a decision matrix in type of Matrix which
can be converted from a DataFrame using the code
```julia
Matrix(df)
```
before calling any method.
## Documentation
Please check out the reference manual [here](https://jbytecode.github.io/JMcDM/).
## Implemented methods
### MCDM Tools
- AHP (Analytic Hierarchy Process)
- ARAS (Additive Ratio Assessment)
- CoCoSo (Combined Compromise Solution)
- CODAS (COmbinative Distance-based ASsessment)
- Copeland (For combining multiple ordering results)
- COPRAS (COmplex PRoportional ASsessment)
- CRITIC (CRiteria Importance Through Intercriteria Correlation)
- DEA (Data Envelopment Analysis)
- DEMATEL (The Decision Making Trial and Evaluation Laboratory)
- EDAS (Evaluation based on Distance from Average Solution)
- ELECTRE (Elimination and Choice Translating Reality)
- Entropy
- GRA (Grey Relational Analysis)
- LMAW (Logarithm Methodology of Additive Weights)
- LOPCOW (LOgarithmic Percentage Change-driven Objective Weighting)
- MABAC (Multi-Attributive Border Approximation area Comparison)
- MAIRCA (Multi Attributive Ideal-Real Comparative Analysis)
- MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution)
- MEREC (MEthod based on the Removal Effects of Criteria) for determining weights
- MOORA Ratio
- MOORA Reference (Multi-Objective Optimization By Ratio Analysis)
- MOOSRA (Multi-Objective Optimization on the basis of Simple Ratio Analysis) Method
- Non-dominated Sorting
- OCRA (Operational Competitiveness RAting)
- PIV (Proximity Indexed Value) method
- PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations)
- PSI (Preference Selection Index) Method
- ROV (Range of Value) Method
- SAW (Simple Additive Weighting) (aka WSM)
- SD Method for determining weights of criteria
- SECA (Simultaneous Evaluation of Criteria and Alternatives)
- TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions)
- VIKOR (VlseKriterijumska Optimizcija I Kaompromisno Resenje in Serbian)
- WASPAS (Weighted Aggregated Sum Product ASsessment)
- WPM (Weighted Product Model)
- TODIM (the Portuguese acronym for multicriteria, interactive decision-making)
- CILOS (The Criterion Impact Loss Method)
- IDOCRIW (Integrated Determination of Objective Criteria Weights)
- Best-Worst Method
- RAM (Root Assessment Method)
### SCDM Tools
- minimax
- maximin
- minimin
- maximax
- Savage
- Hurwicz
- MLE
- Laplace
- Expected Regret
### Game
- Game solver for zero sum games
## Unimplemented methods
- ANP (Analytical Network Process)
- COMET
- Goal Programming
- MACBETH
- MAUT
- ORESTE
- PAPRIKA
- SMAA
- STEM
- SWARA
- UTA
- will be updated soon.
## Example
```julia
julia> using JMcDM
julia> using 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
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
julia> w = [0.35, 0.15, 0.25, 0.20, 0.05];
julia> fns = [minimum, maximum, minimum, minimum, maximum];
julia> result = topsis(Matrix(df), w, fns);
julia> result.scores
3-element Array{Float64,1}:
0.5854753145549456
0.6517997936899308
0.41850223305822903
julia> result.bestIndex
2
```
alternatively
```julia
julia> result = mcdm(Matrix(df), w, fns, TopsisMethod())
```
or
```julia
julia> setting = MCDMSetting(Matrix(df), w, fns)
julia> result = topsis(setting)
```
or
```julia
julia> setting = MCDMSetting(Matrix(df), w, fns)
julia> result = mcdm(setting, TopsisMethod())
```
### Jupyter Notebook
Here is a Jupyter Notebook for basic usage:
https://github.com/jbytecode/JMcDM/blob/main/notebook/basic-usage.ipynb
## Community guidelines
### How to cite
Please use the BibTeX entry:
```bibtex
@article{Satman2021,
doi = {10.21105/joss.03430},
url = {https://doi.org/10.21105/joss.03430},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {65},
pages = {3430},
author = {Mehmet Hakan Satman and Bahadır Fatih Yıldırım and Ersagun Kuruca},
title = {JMcDM: A Julia package for multiple-criteria decision-making tools},
journal = {Journal of Open Source Software}
}
```
or citation string
Satman et al., (2021). JMcDM: A Julia package for multiple-criteria decision-making tools. Journal of Open Source Software, 6(65), 3430, https://doi.org/10.21105/joss.03430
to cite this software.
### Contribute to software
Do you want to contribute?
- Please create an issue first. In this issue, please specify the idea.
- Let the community discuss the new contribution in our Slack channel or the created issue.
If the community decision is yes, please
- Fork the repository
- Add the new code to this forked repository
- Make sure the tests are passed
- Send a pull request with a good description of functionality.
### Where to start?
The TOPSIS method, defined in [topsis.jl](https://github.com/jbytecode/JMcDM/blob/main/src/topsis.jl), is a basis for many methods and it can be followed before implementing a new one.
### The design pattern
- ```topsis()``` takes the decision matrix, weights, and vector of directions of optimization as arguments. This function is defined in ```topsis.jl```.
```julia
function topsis(decisionMat::Matrix, weights::Array{Float64,1}, fns::Array{Function,1})::TopsisResult
```
- ```topsis()``` method has a return type of ```TopsisResult```. This ```struct``` is defined in ```types.jl```
```julia
struct TopsisResult <: MCDMResult
decisionMatrix::Matrix
weights::Array{Float64,1}
normalizedDecisionMatrix::DataFrame
normalizedWeightedDecisionMatrix::DataFrame
bestIndex::Int64
scores::Array{Float64,1}
end
```
- Optionally, a ```show``` function can be derived for pretty-printing the result. These functions are defined in ```print.jl```
```julia
function Base.show(io::IO, result::TopsisResult)
println(io, "Scores:")
println(io, result.scores)
println(io, "Best indices:")
println(io, result.bestIndex)
end
```
Please read the issue [Welcome to newcomers!](https://github.com/jbytecode/JMcDM/issues/3) for other implementation details.
### Report Issues
If you find a bug or error, first report the problem in a new issue. If the problem is already addressed
in an existing issue please follow the existing one.
### Seek Support
Our Slack channel is [JMcDM Slack Channel](https://julialang.slack.com/archives/C01MJ0VF1U3). Please feel free to ask about any problem using our Slack channel or issues. [Julia Discourse](https://discourse.julialang.org/t/jmcdm-a-julia-package-for-multiple-criteria-decision-making-tools/54942) is the JMcDM entry in Julia Discourse site and any thoughts, problems, and issues can also be discussed there.
Welcome!