carlosss1111/Neutrosophic_Decision_Making_Models: v1.0.0 — Reproducibility package

Article A lattice structure on neutrosophic hesitant fuzzy sets with applications in decision making. Overview This record provides a minimal, self-contained implementation of the SVNHFS benchmark used in the paper. The full reference implementation is contained in the notebook Neutrosophic_Decision_Making_Models.ipynb. Running the notebook reproduces the manuscript tables by printing ready-to-paste LaTeX code (decision matrix, criterion baselines, INDS/PDS outputs, and comparative summary). It also performs robustness/stability analyses (Dirichlet perturbations of λ and w, leave-one-criterion-out, simplex grid sweeps) and reports ties-aware rank-agreement metrics (Kendall’s τ_b, Spearman’s ρ, Kendall’s W, etc.). The code uses only the Python standard library (no NumPy/Pandas). Contents Neutrosophic_Decision_Making_Models.ipynb (main notebook: implementation + experiments + LaTeX output). README.md. LICENSE. Quick start Option A — Google Colab Open Neutrosophic_Decision_Making_Models.ipynb in Colab. Run all cells. Option B — Local Jupyter Clone/download the repository. Open the notebook with Jupyter and run all cells. Notes Random seeds and configuration parameters are fixed and documented in the notebook header section. The benchmark dataset is embedded in the notebook (no external data download is required). Citation / DOI Zenodo DOI (version v1.0.0): 10.5281/zenodo.18852635 Zenodo DOI (all versions / concept DOI): 10.5281/zenodo.18852634 Please cite the paper.