IFLR-RANCOM & MABAC FRAMEWORK
Assessment of sustainable hydrogen supply alternatives
Research
Completed
Reference implementation of a fuzzy-linguistic decision framework for evaluating and ranking hydrogen supply pathways under uncertainty. It applies advanced fuzzy-set aggregation and multi-criteria ranking methods, combining intuitionistic fuzzy linguistic rough sets, Sugeno–Weber operators, and a RANCOM + MABAC pipeline. The framework was validated on a comparative case study of hydrogen production options, offering a reproducible computational tool for decision analysis under uncertainty.
A system that turns fuzzy expert opinions (like 'this option is kind of clean but costly') into ranked scores to compare hydrogen options.
Co-author — implemented computational model, aggregation operators, and analysis tooling
Research Contributions
- 01Handles vague/hesitant expert judgments with fuzzy linguistic rough sets
- 02Nonlinear aggregation via Sugeno–Weber operators
- 03Robust criteria weighting using extended RANCOM
- 04Final ranking with combined RANCOM weights and MABAC
- 05Validated through hydrogen supply pathway case study
Technology Stack
PythonNumPyPandasMatplotlibJupyter
Primary Focus: Decision Models · Research Implementation