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RMCDA: The comprehensive R library for applying multi-criteria decision analysis methods

Annice Najafi, Shokoufeh Mirzaei

TL;DR

The paper presents RMCDA, a comprehensive open-source R package that unifies a broad spectrum of MCDA methods, including traditional approaches (AHP, TOPSIS, PROMETHEE, VIKOR) and newer stratified techniques (SMCDM, SBWM), with visualization and easy installation. It situates RMCDA within the R ecosystem by contrasting it with existing libraries and highlighting its expanded method coverage (over 50 methods) and unique offerings, such as SMCDM and SBWM, not widely available elsewhere. The software description outlines a three-part architecture (input, method implementations, visualization) and details key functions, plus specialized implementations for ANP, SMCDM, and SBWM, as well as the ShinyRMCDA interface. Through illustrative installation and three representative examples, the authors demonstrate practical use, performance characteristics, and the potential impact of RMCDA on widening access to MCDA techniques across disciplines. The paper also discusses current limitations (notably the lack of fuzzy MCDA) and outlines future enhancements to improve scalability and extend uncertainty-aware methods, positioning RMCDA as a robust decision-support tool for researchers and practitioners.

Abstract

Multi-Criteria Decision Making (MCDM) is a branch of operations research used in a variety of domains from health care to engineering to facilitate decision-making among multiple options based on specific criteria. Several R packages have been developed for the application of traditional MCDM approaches. However, as the discipline has advanced, many new approaches have emerged, necessitating the development of innovative and comprehensive tools to enhance the accessibility of these methodologies. Here, we introduce RMCDA, a comprehensive and universal R package that offers access to a variety of established MCDM approaches (e.g., AHP, TOPSIS, PROMETHEE, and VIKOR), along with newer techniques such as Stratified MCDM (SMCDM) and the Stratified Best-Worst Method (SBWM). Our open source software intends to broaden the practical use of these methods through supplementary visualization tools and straightforward installation.

RMCDA: The comprehensive R library for applying multi-criteria decision analysis methods

TL;DR

The paper presents RMCDA, a comprehensive open-source R package that unifies a broad spectrum of MCDA methods, including traditional approaches (AHP, TOPSIS, PROMETHEE, VIKOR) and newer stratified techniques (SMCDM, SBWM), with visualization and easy installation. It situates RMCDA within the R ecosystem by contrasting it with existing libraries and highlighting its expanded method coverage (over 50 methods) and unique offerings, such as SMCDM and SBWM, not widely available elsewhere. The software description outlines a three-part architecture (input, method implementations, visualization) and details key functions, plus specialized implementations for ANP, SMCDM, and SBWM, as well as the ShinyRMCDA interface. Through illustrative installation and three representative examples, the authors demonstrate practical use, performance characteristics, and the potential impact of RMCDA on widening access to MCDA techniques across disciplines. The paper also discusses current limitations (notably the lack of fuzzy MCDA) and outlines future enhancements to improve scalability and extend uncertainty-aware methods, positioning RMCDA as a robust decision-support tool for researchers and practitioners.

Abstract

Multi-Criteria Decision Making (MCDM) is a branch of operations research used in a variety of domains from health care to engineering to facilitate decision-making among multiple options based on specific criteria. Several R packages have been developed for the application of traditional MCDM approaches. However, as the discipline has advanced, many new approaches have emerged, necessitating the development of innovative and comprehensive tools to enhance the accessibility of these methodologies. Here, we introduce RMCDA, a comprehensive and universal R package that offers access to a variety of established MCDM approaches (e.g., AHP, TOPSIS, PROMETHEE, and VIKOR), along with newer techniques such as Stratified MCDM (SMCDM) and the Stratified Best-Worst Method (SBWM). Our open source software intends to broaden the practical use of these methods through supplementary visualization tools and straightforward installation.

Paper Structure

This paper contains 17 sections, 1 equation, 9 figures, 4 tables.

Table of Contents

  1. Motivation and significance
  2. Comparison with existing R libraries and other tools
  3. Software description and architecture
  4. Overview of key functions
  5. Software functionalities
  6. Analytical Network Process (ANP)
  7. Stratified MCDM (SMCDM)
  8. Stratified Best Worst Method (SBWM)
  9. ShinyRMCDA
  10. Illustrative examples
  11. Installation
  12. Example I.
  13. Example II.
  14. Example III.
  15. Algorithm Run Time
  16. ...and 2 more sections

Figures (9)

  • Figure 1: A-E show the number of downloads over time by package. F compares the number of supported methods by package.
  • Figure 2: Flowchart demonstrating how the strata and states are structured in the SMCDM method with three events which occur independent of each other.
  • Figure 3: Overview of the ShinyRMCDA application.
  • Figure 4: Format of the input CSV file for AHP.
  • Figure 5: Visualization outputs from the RMCDA package for AHP.
  • ...and 4 more figures