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

Area of Science:

• Industrial Engineering
• Operations Research
• Sustainable Manufacturing

Background:

• Industry 4.0 emphasizes energy efficiency in smart factories for sustainable growth.
• Decision-making for energy-saving solutions is complex due to uncertainty and ambiguity.
• Existing methods struggle to adequately address vagueness in criteria assessments.

Purpose of the Study:

• To propose a novel multi-criteria decision-making (MCDM) framework for selecting energy-saving solutions in Industry 4.0 smart factories.
• To develop new intuitionistic fuzzy set (IFS) based aggregation operators (AOs) to manage uncertainty and vagueness.
• To provide a robust method for determining criteria weights and ranking alternatives.

Main Methods:

• Development of novel aggregation operators: intuitionistic fuzzy softmax Dubois-Prade (IFSDP), IFSIDPWA, IFSIDPOWA, and IFSIDPWG.
• Utilization of the Method based on the Removal Effects of Criteria (MEREC) for objective criteria weighting.
• Application of the Ranking Alternatives using Functional mapping of Criteria sub-intervals into a Single Interval (RAFSI) approach for alternative evaluation.

Main Results:

• The proposed MCDM framework effectively handles uncertainty and vagueness in decision-making processes.
• The novel AOs and MEREC method provide a robust structure for evaluating energy-saving solutions.
• The RAFSI approach successfully ranks alternatives, demonstrating the framework's practical applicability.

Conclusions:

• The developed framework offers a reliable approach to identifying optimal energy-saving solutions for smart factories.
• This research supports sustainable manufacturing practices within the Industry 4.0 paradigm.
• The findings guide decision-makers towards greener and more efficient industrial operations.