Integrating intuitionistic fuzzy and MCDM methods for sustainable energy management in smart factories.
Journal:
PloS one
PMID:
39808640
Abstract
Improving energy efficiency is crucial for smart factories that want to meet sustainability goals and operational excellence. This study introduces a novel decision-making framework to optimize energy efficiency in smart manufacturing environments, integrating Intuitionistic Fuzzy Sets (IFS) with Multi-Criteria Decision-Making (MCDM) techniques. The proposed approach addresses key challenges, including reducing carbon footprints, managing operating costs, and adhering to stringent environmental standards. Eight essential criteria are identified, such as the use of renewable energy, the efficiency of production, and the health and safety of workers, to evaluate energy performance. Using the entropy method for criterion weighting and the CRADIS technique for alternative ranking, we prioritize a range of energy-efficient solutions. The novelty of our approach lies in its comprehensive assessment of complex real-world energy management scenarios within smart factories, offering a robust and adaptable decision-support tool. Our empirical results, validated through sensitivity analysis, show that alternative 5 delivers the most significant improvement in energy efficiency. This study provides valuable information for industry practitioners seeking to transition to more sustainable production methods and supports the broader sustainability agenda.