Optimizing sustainability: information aggregation in smart factories for energy efficiency enhancement

Abstract

In the context of Industry 4.0, improving energy efficiency in smart factories has emerged as a key priority to drive sustainable industrial growth. However, identifying optimal energy-saving solutions is challenging due to the inherent uncertainty and complexity in decision-making. This study addresses these challenges by proposing a multi-criteria decision-making (MCDM) framework that leverages intuitionistic fuzzy sets (IFSs) to manage ambiguity in the evaluation process. To advance this framework, we develop a suite of novel aggregation operators (AOs), including the intuitionistic fuzzy softmax Dubois-Prade (IFSDP), intuitionistic fuzzy softmax interactive Dubois-Prade weighted average (IFSIDPWA), intuitionistic fuzzy softmax interactive Dubois-Prade ordered weighted average (IFSIDPOWA), and intuitionistic fuzzy softmax interactive Dubois-Prade weighted geometric (IFSIDPWG), which effectively handle uncertainty and vagueness in the criteria assessments. The method based on the removal effects of criteria (MEREC) is utilized to objectively determine the criteria weights, ensuring a robust evaluation structure. For ranking, the alternatives are evaluated through the ranking of alternatives using functional mapping of criteria sub-intervals into a Single Interval (RAFSI) approach. A case study involving eight criteria and five energy-saving solutions demonstrates the framework’s feasibility, with results confirming the effectiveness of our AOs and RAFSI technique in guiding decision-makers toward sustainable energy solutions for smart factories. This framework is poised to support sustainable manufacturing practices in Industry 4.0, fostering greener and more efficient industrial operations.