## Sustainable Energy Management in Smart Factories with Intuitionistic Fuzzy Decision-Making Framework### IntroductionIn the era of Industry 4.0, smart factories serve as the keystone of contemporary industrial production due to advances in technologies like the Internet of Things (IoT), artificial intelligence (AI), and big data analytics. These factories are distinguished by characteristics such as self-optimization, adaptability to changing conditions, and autonomous management of the entire production process. One way to characterize a smart factory would be through the use of cyber-physical systems to track real-time activities, digitally replicate those actions, and make autonomous choices. With the help of the IoT, these networks can now interact and collaborate in real-time with both individuals and other networks. This makes it possible for information to move freely throughout the entire production network. Smart factories offer several advantages, such as increased productivity, automation of monotonous tasks, and rapid responses to changes in market and customer expectations due to the additional flexibility they offer. They also enhance worker safety by automating risky operations, lowering downtime by anticipating maintenance needs, and improving product quality through precise management and process monitoring. Managing complicated systems, a task that frequently fails when standard methods are employed, is one of the activities that greatly benefit from this.### Literature ReviewTo make sustainability a reality in the industrial sector, it is imperative to implement sustainable manufacturing practices [4]. For the industry at large, sustainability entails more than just a passing trend. Emissions reduction, community and worker protection, and responsible energy management are just a few of the many goals of sustainable manufacturing. It involves a variety of actions, such as reducing waste, recycling, conserving resources, and using renewable, environmentally friendly energy sources. Energy efficiency, as emphasized in [5], plays a crucial role in the industrial sector in lessening the financial strain on businesses and the detrimental effects on the environment. Energy efficiency has an impact on both the bottom line and the release of greenhouse gases. Hence, enhancing energy efficiency makes financial and environmental sense. Numerous approaches for increasing energy efficiency in smart factories include equipment optimization, adoption of energy-efficient technologies and procedures, and implementation of advanced energy management systems [6].### Integrated Intuitionistic Fuzzy and MCDM Framework for Sustainable Energy ManagementWe intend to examine the potential of sustainable smart factories to enhance energy efficiency from the perspective of intuitionistic fuzzy (IF) sets in this study. We will be able to achieve our goal of creating a decision-support system that can rank and assess different options for raising energy efficiency levels by utilizing MCDM methodologies inside this framework. This approach, which aligns with broader sustainability goals and considers both the operational and technological elements of energy management, guarantees a significant advancement in creating environmentally friendly manufacturing processes. An overview of the areas that will be covered in this article is provided below: This study is divided into the following sections: a comprehensive literature review on smart factories, manufacturing sustainability, and the application of IF in MCDM; a methodology outlining criteria selection, alternative identification, and MCDM technique application within an IF environment; a case study demonstrating the practical implementation of our proposed approach; analysis and discussion of the findings; and, finally, conclusions emphasizing the contributions, implications, and future directions for research.### Results and DiscussionOur empirical findings, verified through sensitivity analysis, show that alternative 5 offers the most substantial increase in energy efficiency. This study gives insightful information for those in the business who want to switch to environmentally friendly production methods while also supporting the sustainability agenda on a wider scale. Standard decision-making approaches are frequently insufficient because of the inherently imprecise and subjective assessments that come with considering a huge number of factors. As a result, the value of Intuitionistic Fuzzy (IF) Sets cannot be overstated. In the context of multi-criteria decision-making, IF sets are more powerful than fuzzy sets since they can capture reluctance, membership, and non-membership levels more thoroughly. Complex contexts like smart factories are especially suited for MCDM because they can capture uncertainty in a more practical and nuanced way. They give a more accurate depiction of uncertainty.### ConclusionTo investigate prospective ways for sustainable smart factories to enhance energy efficiency, this research used an IF viewpoint and incorporated MCDM approaches. We were better able to analyze and prioritize various energy-efficient solutions by utilizing entropy for criterion weighting and CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) for alternative ranking within an IF framework. This article offers a unique way to assess complex energy management issues encountered in smart factories and offers a reliable and adaptable decision-support tool. Alternative 5's superiority in energy efficiency improvement was strongly supported by the analysis and discussion. The paper's contributions, practical ramifications, and future lines of research are all thoroughly examined in the conclusion.### Future Research DirectionsFor future research, we suggest the following directions:- Incorporating dynamic criteria into the framework design to account for changing conditions and manufacturing process adjustments. The decision-making approach can become more responsive and adaptive as a result.- Researching the combination of the framework with ideas from the circular economy for the purpose of enhancing sustainable manufacturing, reducing waste output, and boosting resource utilization.- To guarantee that it performs as desired across a variety of operating situations and sectors, more case studies and empirical validation of the framework are necessary. This will broaden the framework's applicability and dependability.