Framework for Assessing Sustainability in Industry 4.0: Supply Chain Optimization, Manufacturing Processes, and Energy Consumption Reduction

Mohamed Abouhawwash, Department of Industrial and Systems Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia AND Interdisciplinary Research Center of Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum & AND Minerals Dhahran 31261, Saudi Arabia Follow
Nebojsa Bacanin, Singidunum University, Danijelova 32, Belgrade, 11000, Serbia AND Department of Mathematics, Saveetha School of Engineering, SIMATS, Thandalam, Chennai, 602105, IndiaFollow

ORCID

Mohamed Abouhawwash: https://orcid.org/0000-0003-2846-4707

Nebojsa Bacanin: https://orcid.org/0000-0002-2062-924X

Keywords

Neutrosophic set, Uncertainty information, Sustainability, Industry 4.0, Supply chain, Manufacturing processes

Article Type

Original Article

Abstract

Decision making has uncertainty problems in evaluation process, so to overcome uncertainty existing fuzzy decision-making approaches, the neutrosophic set is used to solve incomplete data. Interval valued neutrosophic numbers (IVNNs) are used in this study to solve uncertainty problems. The IVNNs are used with the multi-criteria decision making (MCDM) methods. MCDM method is used to deal with different criteria and alternatives. This study proposes an MCDM methodology for assessment sustainability in industry 4.0. Twenty cases and ten alternatives are used in this study. The criteria weights are computed using the average method. The alternatives are ranked using the ARAS method. Application is shown to show the validation of the proposed approach. Sensitivity analysis is conducted in this study to show the stability of the ranks. The results show the ranks of alternatives are stable under 20 different cases.

How to Cite

Abouhawwash, Mohamed and Bacanin, Nebojsa (2025) "Framework for Assessing Sustainability in Industry 4.0: Supply Chain Optimization, Manufacturing Processes, and Energy Consumption Reduction," Sustainable Machine Intelligence Journal: Vol. 12: Iss. 1, Article 5.
Available at: https://smij.sciencesforce.com/journal/vol12/iss1/5