AccScience Publishing / IJOCTA / Online First / DOI: 10.36922/IJOCTA025360152
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RESEARCH ARTICLE

A fuzzy–digital twin optimization framework for simultaneous management of waste and energy consumption in sustainable manufacturing

Hamed Nozari1* ,  Zornitsa Yordanova2
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1 Department of Management, Azad University United Arab Emirates, Dubai, United Arab Emirates
2 Industrial Business Department, Business Faculty,University of National and World Economy, Sofia, Bulgaria
IJOCTA, 025360152 https://doi.org/10.36922/IJOCTA025360152
Received: 7 September 2025 | Revised: 21 October 2025 | Accepted: 29 October 2025 | Published online: 24 November 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Sustainable manufacturing systems require intelligent methods to balance economic performance with environmental responsibility. This research presents a digital twin-fuzzy multi-objective optimization framework for simultaneously managing cost, energy consumption, and waste in sustainable manufacturing. In this framework, fuzzy logic is used to model data uncertainty, a digital twin is used to obtain real-time data from the manufacturing process, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to generate a Pareto front and analyze the relationships between economic and environmental objectives. The proposed model was tested in 10 simulated scenarios based on digital twin data. The results showed that the proposed framework maintained the service level above 95%, reduced the total cost by 14% and the amount of waste by 18% compared to the baseline. Pareto front analysis also showed that although there is a relative conflict between economic and environmental objectives, this conflict is controllable. Also, sensitivity analysis revealed that energy ceiling and machinery efficiency have the greatest impact on the sustainability and profitability of the system. Overall, the proposed framework provides a reliable, quantitative decision-making tool for managers and policymakers on the path to green and sustainable production.

Graphical abstract
Keywords
Digital twin
Fuzzy logic
Multi-objective optimization
Sustainable production
Waste
Funding
This study was financially supported by the UNWE Research Program.
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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