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A package for Multiple criteria decision-making techniques in Julia

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# 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 chosen 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
```

```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

### Weighting methods

- Custom Weights
- CILOS (The Criterion Impact Loss Method)
- CRITIC (CRiteria Importance Through Intercriteria Correlation)
- Entropy
- IDOCRIW (Integrated Determination of Objective Criteria Weights)
- LOPCOW (LOgarithmic Percentage Change-driven Objective Weighting)
- MEREC (MEthod based on the Removal Effects of Criteria) for determining weights
- SD Method for determining weights of criteria

### Classical 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)
- 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)
- GRA (Grey Relational Analysis)
- LMAW (Logarithm Methodology of Additive Weights)
- MABAC (Multi-Attributive Border Approximation area Comparison)
- MAIRCA (Multi Attributive Ideal-Real Comparative Analysis)
- MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution)
- 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)
- 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)
- Best-Worst Method
- RAM (Root Assessment Method)
- CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) 
- SPOTIS (Stable Preference Ordering Towards Ideal Solution)

### Fuzzy MCDM Tools

- Fuzzy CoCoSo
- Fuzzy EDAS 
- Fuzzy TOPSIS
- Fuzzy SAW
- Fuzzy VIKOR

### SCDM Tools

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

### Game

- Game solver for zero sum games

## 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())
```

```julia
julia> setting = MCDMSetting(Matrix(df), w, fns)
julia> result = topsis(setting)
```

```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.

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