Search Results (57)

In response to the challenges of urbanization, digitalization, and the e-commerce surge intensified by the COVID-19 pandemic, Smart Urban Logistics (SUL) has become a key framework for addressing last-mile delivery issues, congestion, and environmental impacts. This study introduces a System Dynamics (SD)-based approach to investigate how urban logistics and access management policies may interact. At the center, there is a Causal Loop Diagram (CLD) that illustrates dynamic interdependencies among fleet composition, access regulations, logistics productivity, and environmental externalities. The CLD is a conceptual basis for future stock-and-flow simulations to support data-driven decision-making. The approach highlights the importance of route optimization, dynamic access control, and smart parking management systems as strategic tools, increasingly enabled by Industry 4.0 technologies, such as IoT, big data analytics, AI, and cyber-physical systems, which support real-time monitoring and adaptive planning. In alignment with the Industry 5.0 paradigm, this technological integration is paired with social and environmental sustainability goals. The study also emphasizes public–private collaboration in designing access policies and promoting alternative fuel vehicle adoption, supported by specific incentives. These coordinated efforts contribute to achieving the objectives of the 2030 Agenda, fostering a cleaner, more efficient, and inclusive urban logistics ecosystem. Full article

In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling (APS) systems, particularly under finite-capacity constraints. Traditional scheduling models often overlook real-time resource limitations, leading to inefficiencies in complex and dynamic production environments. AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. These AI-powered APS systems enhance scheduling accuracy, reduce lead times, improve resource utilization, and enable the proactive identification of production bottlenecks. Especially relevant in high-variability sectors like fashion, these approaches support Industry 5.0 goals by enabling agile, sustainable, and human-centered manufacturing systems. The findings have been highlighted in a structured framework for AI-based APS systems supported by metaheuristics that compares the Industry 4.0 and Industry 5.0 perspectives. The study offers valuable implications for both academia and industry: academics can gain a synthesized understanding of emerging trends, while practitioners are provided with actionable insights for deploying intelligent planning systems that align with sustainability goals and operational efficiency in modern supply chains. Full article

The European Union (EU) is striving to achieve its goal of being climate-neutral by 2050. Aligned with the European Green Deal and in search of means to decarbonize its urban environments, the EU advocates for smart positive energy districts (PEDs). PEDs contribute to the United Nations’ (UN) sustainable development goals (SDGs) of “Sustainable Cities and Communities”, “Affordable and Clean Energy”, and “Climate Action”. PEDs are urban neighborhoods that generate renewable energy to a higher extent than they consume, mainly through the utilization of innovative technologies and renewable energy sources. In accordance with the EU 2050 aim, the PED concept is attracting growing research interest. PEDs can transform existing energy systems and aid in achieving carbon neutrality and sustainable urban development. PED is a novel concept and its implementation is challenging. This study aims to present the emerging technologies enabling the proliferation of PEDs by identifying the main challenges and potential solutions to effective adoption and implementation of PEDs. This paper examines the importance and utilization of cyber-physical systems (CPSs), digital twins (DTs), artificial intelligence (AI), the Internet of Things (IoT), edge computing, and blockchain technologies, which are all fundamental to the creation of PEDs for enhancing energy efficiency, sustainable energy, and user engagement. These systems combine physical infrastructure with digital technologies to create intelligent and autonomous systems to optimize energy production, distribution, and consumption, thus positively contributing to achieving smart and sustainable development. Full article

During this period, as distributed energy resources (DERs) are crucial for meeting energy needs and renewable technology advances rapidly, photovoltaic (PV)-powered energy islands (EIs) requiring a constant energy supply have emerged. EIs represent a significant milestone in the global energy transformation towards clean and sustainable energy production. They play a vital role in the future energy infrastructure, offering both environmental and economic benefits. In this context, reliance on information and communication technologies for system management raises concerns regarding the cybersecurity vulnerabilities of PV-supported EIs. In other words, since EIs transmit power through power converters—integral cyber-physical components of these systems—they are uniquely susceptible to cyber-attacks. To tackle this vulnerability, a cyber-attack detection scheme using a supervised machine learning (SML) model is proposed. The initial goal is to ensure the transfer and maintenance of energy demands without power loss for critical loads by detecting cyber-attacks to establish a defense mechanism. Two distinct artificial neural network (ANN) structures are implemented to identify cyber threats and support subsequent power demand, resulting in a complementary approach. The findings reveal the model’s effectiveness, demonstrating high accuracy (e.g., a cross-entropy loss of 12.842 × 10⁻⁴ for ANN-I with a 99.98% F1 score and an MSE of 1.0934 × 10⁻⁷ for ANN-II). Therefore, this work aims to open the fundamental way for addressing this issue, particularly concerning hijacking attacks and false data injection (FDI) cyber-attacks on PV-powered EIs. The success of this model and its outcomes confirm the effectiveness of the proposed approach method. Full article

The objective of this study is to conduct an analysis of the scientific literature on the application of the Internet of Things (IoT) and artificial intelligence (AI) in enhancing supply chain operations. This research applies a dual approach combining bibliometric analysis and topic modeling to explore both quantitative citation trends and qualitative thematic insights. By examining 810 qualified articles, published between 2011 and 2024, this research aims to identify the main topics, key authors, influential sources, and the most-cited articles within the literature. The study addresses critical research questions on the state of IoT and AI integration into supply chains and the role of these technologies in resolving digital supply chain management challenges. The convergence of IoT and AI holds immense potential to redefine supply chain management practices, improving productivity, visibility, and sustainability in interconnected global supply chains. This research not only highlights the continuous evolution of the supply chain field in light of Industry 4.0 technologies—such as machine learning, big data analytics, cloud computing, cyber–physical systems, and 5G networks—but also provides an updated overview of advanced IoT and AI technologies currently applied in supply chain operations, documenting their evolution from rudimentary stages to their current state of advancement. Full article

Artificial intelligence (AI) has become a revolutionary tool in the automotive sector, specifically in quality management and issue identification. This article presents a systematic review of AI implementations whose target is to enhance production processes within Industry 4.0 and 5.0. The main methods analyzed are deep learning, artificial neural networks, and principal component analysis, which improve defect detection, process automation, and predictive maintenance. The manuscript emphasizes AI’s role in live auto part tracking, decreasing dependance on manual inspections, and boosting zero-defect manufacturing strategies. The findings indicate that AI quality control tools, like convolutional neural networks for computer vision inspections, considerably strengthen fault identification precision while reducing material scrap. Furthermore, AI allows proactive maintenance by predicting machine defects before they happen. The study points out the importance of incorporating AI solutions in actual manufacturing methods to ensure consistent adaptation to Industry 5.0 requirements. Future investigations should prioritize transparent AI approaches, cyber-physical system consolidation, and AI material enhancement for sustainable production. In general terms, AI is changing quality assurance in the automotive industry, improving efficiency, consistency, and long-term results. Full article

This paper presents a conceptual framework aimed at integrating Digital Twins and cyber-physical production systems into the energy management of manufacturing facilities. To address the challenges of rising energy costs and environmental impacts, this framework combines digital modeling and customized energy management for direct manufacturing operations. Through a review of the existing literature, essential components such as physical models, a data platform, an energy optimization platform, and various interfaces are identified. Key requirements are defined in terms of functionality, performance, reliability, safety, and additional factors. The proposed framework includes the physical system, data platform, energy management system, and interfaces for both operators and external parties. The goal of this framework is to set the basis for allowing manufacturers to reduce energy consumption and costs during the lifecycle of assets more effectively, thereby improving energy efficiency in smart manufacturing. The study highlights opportunities for further research, such as real-world applications and sophisticated optimization methods. The advancement of Digital Twin technologies holds significant potential for creating more sustainable factories. Full article

The Fourth Industrial Revolution, also known as Industry 4.0, which is the intensified digitalization and automation in industry, embraces cyber–physical systems, the Internet of Things (IoT), and artificial intelligence, among others. This study utilizes Fuzzy Integration–Rough Set Theory (FI-RST) analysis to quantify the impacts of the imperative Industry 4.0 technologies for manufacturing firms located in Fujian Province, China, namely, Manufacturing Execution Systems (MES), the Industrial Internet of Things (IIoT), and Additive Manufacturing (AM), on the sustainable development performance of firms. The findings of the study indicate that these technologies greatly improve the effectiveness of the utilization of resources, reduce the costs of operations, and reduce the impact on the environment. In addition, they have a favorable influence on social considerations, such as preserving the well-being of employees and the outcome of training programs. This research work has convincingly provided an underlying strategic adoption of these technologies for sustainability production by raising important insights that could be valuable for industry managers and policymakers, especially those seeking sustainability at the global level. Full article

Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers’ order requirements and mitigate negative impacts on the environment. The planning of processes to achieve sustainable CPSs becomes an important issue to meet demand timely in a dynamic environment. The problem with planning processes in sustainable CPSs is the determination of the configuration of workflows/resources to compose processes with desirable properties, taking into account time and energy consumption factors. The planning problem in sustainable CPSs can be formulated as an integer programming problem with constraints, and this poses a challenge due to computational complexity. Furthermore, the ever-shrinking life cycle of technologies leads to frequent changes in processes and makes the planning of processes a challenging task. To plan processes in a changing environment, an effective planning method must be developed to automate the planning task. To tackle computational complexity, evolutionary computation approaches such as bio-inspired computing and metaheuristics have been adopted extensively in solving complex optimization problems. This paper aims to propose a solution methodology and an effective evolutionary algorithm with a local search mechanism to support the planning of processes in sustainable CPSs based on an auction mechanism. To achieve this goal, we focus on developing a self-adaptive neighborhood search-based Differential Evolution method. An effective planning method should be robust in terms of performance with respect to algorithmic parameters. We assess the performance and robustness of this approach by performing experiments for several cases. By comparing the results of these experiments, it shows that the proposed method outperforms several other algorithms in the literature. To illustrate the robustness of the proposed self-adaptive algorithm, experiments with different settings of algorithmic parameters were conducted. The results show that the proposed self-adaptive algorithm is robust with respect to algorithmic parameters. Full article

Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI’s ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper’s contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors’ knowledge and experience in renewable energy, AI, and mechatronics. Full article

Cyber–physical systems involve the creation, continuous updating, and monitoring of virtual replicas that closely mirror their physical counterparts. These virtual representations are fed by real-time data from sensors, Internet of Things (IoT) devices, and other sources, enabling a dynamic and accurate reflection of the state of the physical system. This emphasizes the importance of data synchronization, visualization, and interaction within virtual environments as a means to improve decision-making, training, maintenance, and overall operational efficiency. This paper presents a novel approach to a cyber–physical system that integrates virtual reality (VR)-based digital twins and 3D SCADA in the context of Industry 4.0 for the monitoring and optimization of an olive mill. The methodology leverages virtual reality to create a digital twin that enables immersive data-driven simulations for olive mill monitoring. The proposed CPS takes data from the physical environment through the existing sensors and measurement elements in the olive mill, concentrates them, and exposes them to the virtual environment through the Open Platform Communication United Architecture (OPC-UA) protocol, thus establishing bidirectional and real-time communication. Furthermore, in the proposed virtual environment, the digital twin is interfaced with the 3D SCADA system, allowing it to create virtual models of the process. This innovative approach has the potential to revolutionize the olive oil industry by improving operational efficiency, product quality, and sustainability while optimizing maintenance practices. Full article

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system. Full article

Cyber-physical systems, cloud computing, the Internet of Things, and big data play significant roles in shaping digital and automated landscape manufacturing. However, to fully realize the potential of these technologies and achieve tangible benefits, such as reduced manufacturing lead times, improved product quality, and enhanced organizational performance, new decision support models need development. Game theory offers a promising approach to address multi-objective problems and streamline decision-making processes, thereby reducing computational time. This paper aims to provide a comprehensive and up-to-date systematic review of the literature on the application of game theory models in various areas of digital manufacturing, including production and capacity planning, scheduling, sustainable production systems, and cloud manufacturing. This review identifies key research themes that have been explored and examines the main research gaps that exist within these domains. Furthermore, this paper outlines potential future research directions to inspire both researchers and practitioners to further explore and develop game theory models that can effectively support the digital transformation of manufacturing systems. Full article

Nowadays, managers are facing the challenge of operating in situations of high uncertainty: delayed deliveries, lack of energy or rising energy and gas costs, the need to replace energy sources, and changing supply and sales markets. In the literature, two dominant trends in the activities of enterprises in the face of crises can be distinguished: (I) changes in supply chain management (increased flexibility by searching for local suppliers); and (II) transition to digital production and investment in technologies in the concept of Industry 4.0 or even Industry 5.0, such as artificial intelligence, 3D printing, robots, cyber-physical systems, digital manufacturing, and blockchain. A gap in the research has been observed in examining the impacts of these actions on the implementation of sustainable solutions and designating organizational changes in manufacturing. The main goal of this study is to review the literature using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) for data collection and, secondly, the methodology of Systematic Literature Review (SLR) and Mapping in Literature Reviews (MLR). Our literature review of the selected databases is based on 566 published articles in 2020–2022. The achieved results indicate the main organizational changes in the context of sustainable development in manufacturing, namely in the business management area (adopting Sustainable Project Management (SPM), Sustainable Supply Chain Management practices, Sustainable Supplier Selection (SSS), and Resilient Manufacturing Strategy (RMS)) and in the production area (adopting Internet of Things (IoT)-enabled Additive Manufacturing assists, simulation software, and Life Cycle Assessment. The findings of our study revealed key relationships between the adoption of fifth-generation industrial technologies and the sustainable development of manufacturing. Full article

Digital twins are emerging as a prime analysis, prediction, and control concepts for enabling the Industrie 4.0 vision of cyber-physical production systems (CPPSs). Today’s growing complexity and volatility cannot be handled by monolithic digital twins but require a fundamentally decentralized paradigm of cooperating digital twins. Moreover, societal trends such as worldwide urbanization and growing emphasis on sustainability highlight competing goals that must be reflected not just in cooperating but also competing digital twins, often even interacting in “coopetition”. This paper argues for multi-agent systems (MASs) to address this challenge, using the example of embedding industrial digital twins into an urban planning context. We provide a technical discussion of suitable MAS frameworks and interaction protocols; data architecture options for efficient data supply from heterogeneous sensor streams and sovereignty in data sharing; and strategic analysis for scoping a digital twin systems design among domain experts and decision makers. To illustrate the way still in front of research and practice, the paper reviews some success stories of MASs in Industrie/Logistics 4.0 settings and sketches a comprehensive vision for digital twin-based holistic urban planning. Full article