An intelligent decision model for optimizing industrial power consumption using q-fraction fuzzy information

Area of Science:

• Industrial Engineering
• Operations Research
• Environmental Science

Background:

• Industrial power consumption presents a significant challenge for ecological sustainability.
• Optimizing energy usage is crucial for reducing operational costs and environmental impact.
• Existing optimization methods often struggle with uncertainties in industrial energy data.

Purpose of the Study:

• To propose a novel q-fractional fuzzy logic decision-making model for industrial energy consumption management.
• To integrate multiple criteria such as machine power requirements, efficiency, time constraints, and energy prices.
• To address uncertainties and imprecision in energy consumption data within industrial settings.

Main Methods:

• Utilizing q-fractional fuzzy aggregation operators including Weighted Average (WA), Weighted Geometric (WG), Ordered Weighted Average (OWA), Ordered Weighted Geometric (OWG), Hybrid Average (HA), and Hybrid Geometric (HG).
• Developing a decision-making framework that combines various industrial energy consumption parameters.
• Employing case studies and simulations to validate the model's performance.

Main Results:

• The q-fractional fuzzy logic model demonstrated a substantial decrease in industrial energy consumption compared to traditional optimization methods.
• The proposed model significantly outperformed the competitive CoCoSo method in reliability and effectiveness.
• Achieved high-quality, cost-efficient production strategies through energy savings.

Conclusions:

• The q-fractional fuzzy set is a feasible and intelligent technology for managing industrial energy consumption towards ecological sustainability.
• The developed q-fraction fuzzy verdict paradigm effectively reduces industrial power usage with minimal human intervention.
• This approach offers a robust solution for optimizing energy management in industrial processes.