Empirical Dataset and Computational Implementation of the Hybrid AHP–Entropy–TOPSIS Model for Supplier Selection

This repository contains the results of the empirical phase of my dissertation, titled “Hybrid Multicriteria Model Integrated with Business Intelligence to Optimize Supplier Selection.” It includes the dataset zenodo_dataset.csv, which contains the values of five suppliers evaluated according to four criteria (Cost, Quality, Delivery, and Flexibility), as well as the R compilation report of proposed prototype Met_Hybrid_AHP-Entropy-TOPSIS, which implements a semi-automated version of the hybrid AHP–Entropy–TOPSIS method for optimal supplier selection. Both files enable full reproduction of the example presented in the dissertation (hypothetical case). In addition, a script with execution times and a performance analysis of the model (first simulation) is provided. A second simulation is also included, used as a cross-validation and comparative analysis with the case of Chen (2020), based on the original data presented in that study (for comparative purposes only). In this second simulation, the model is run again incorporating Chen’s (2020) initial data and performing criterion-by-criterion exclusions (compilation file available), with the aim of analyzing the behavior of the results and generating the data table required for the subsequent sensitivity analysis, which was performed in SPSS (file available). Legal notice: No license granted. All rights reserved. Copying, modification, or redistribution of any part of this material is prohibited without prior written permission from the author. Materials are made available solely for academic record purposes.