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Search Results (213)

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Keywords = multiagent system (MAS)

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25 pages, 928 KiB  
Article
Digital Trust in Transition: Student Perceptions of AI-Enhanced Learning for Sustainable Educational Futures
by Aikumis Omirali, Kanat Kozhakhmet and Rakhima Zhumaliyeva
Sustainability 2025, 17(17), 7567; https://doi.org/10.3390/su17177567 - 22 Aug 2025
Viewed by 273
Abstract
In the context of the rapid digitalization of higher education, proactive artificial intelligence (AI) agents embedded within multi-agent systems (MAS) offer new opportunities for personalized learning, improved quality of education, and alignment with sustainable development goals. This study aims to analyze how such [...] Read more.
In the context of the rapid digitalization of higher education, proactive artificial intelligence (AI) agents embedded within multi-agent systems (MAS) offer new opportunities for personalized learning, improved quality of education, and alignment with sustainable development goals. This study aims to analyze how such AI solutions are perceived by students at Narxoz University (Kazakhstan) prior to their practical implementation. The research focuses on four key aspects: the level of student trust in AI agents, perceived educational value, concerns related to privacy and autonomy, and individual readiness to use MAS tools. The article also explores how these solutions align with the Sustainable Development Goals—specifically SDG 4 (“Quality Education”) and SDG 8 (“Decent Work and Economic Growth”)—through the development of digital competencies and more equitable access to education. Methodologically, the study combines a bibliometric literature analysis, a theoretical review of pedagogical and technological MAS concepts, and a quantitative survey (n = 150) of students. The results reveal a high level of student interest in AI agents and a general readiness to use them, although this is tempered by moderate trust and significant ethical concerns. The findings suggest that the successful integration of AI into educational environments requires a strategic approach from university leadership, including change management, trust-building, and staff development. Thus, MAS technologies are viewed not only as technical innovations but also as managerial advancements that contribute to the creation of a sustainable, human-centered digital pedagogy. Full article
(This article belongs to the Special Issue Sustainable Management for the Future of Education Systems)
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27 pages, 1056 KiB  
Article
Binary Grey Wolf Optimization Algorithm-Based Load Scheduling Using a Multi-Agent System in a Grid-Tied Solar Microgrid
by Sujo Vasu, P Ramesh Kumar and E A Jasmin
Energies 2025, 18(16), 4423; https://doi.org/10.3390/en18164423 - 19 Aug 2025
Viewed by 202
Abstract
Microgrids play a crucial role in the development of future smart grids, with multiple interconnected microgrids forming large-scale multi-microgrid systems that operate as smart grids. Multi-agent system (MAS)-based control solutions are the most suitable for addressing such control challenges. This paper presents a [...] Read more.
Microgrids play a crucial role in the development of future smart grids, with multiple interconnected microgrids forming large-scale multi-microgrid systems that operate as smart grids. Multi-agent system (MAS)-based control solutions are the most suitable for addressing such control challenges. This paper presents a demand-side management (DSM) strategy using a meta-heuristic optimization technique for minimizing the household energy consumption cost using MAS. The binary grey wolf optimization algorithm (BGWOA) optimizes load scheduling, reducing electricity costs, without compromising consumer preferences using time-of-day (ToD) tariffs. The communication agents and load agents comprise the MAS used to streamline load control operations. The results demonstrate that MAS-based load control using metaheuristic optimization techniques enhances demand-side management, thus minimizing the electricity costs while adhering to contradictory parameters like user preferences, appliance duration, and load atomicity. This makes renewable energy integration more cost-effective in smart grids, thereby ensuring affordable, reliable, and sustainable energy for all. Full article
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14 pages, 1820 KiB  
Article
Discrete Event Simulation Based on a Multi-Agent System for Japanese Rice Harvesting Operations
by Malte Grosse, Kiyoshi Honda, Peter Thies and Cornelius Specht
Agriculture 2025, 15(16), 1745; https://doi.org/10.3390/agriculture15161745 - 15 Aug 2025
Viewed by 412
Abstract
Existing rice harvesting models often lack depth or extensibility and are limited in their scope across the agriculture value chain, from crop planting to postharvest handling. A multi-agent system (MAS) offers flexibility and scalability and supports the simulation and modeling of complex real-world [...] Read more.
Existing rice harvesting models often lack depth or extensibility and are limited in their scope across the agriculture value chain, from crop planting to postharvest handling. A multi-agent system (MAS) offers flexibility and scalability and supports the simulation and modeling of complex real-world scenarios. This paper introduces a novel approach utilizing an MAS to simulate rice harvesting operations (including additional pre- and post-harvesting operations). Initially, a generic MAS was created, and it was then subsequently adapted to the agricultural context of rice farming in Central Japan. The localized MAS consists of agents such as weather, farm, rice centers, fields, crops and multiple agriculture machinery. Additionally, the introduced MAS environment is based on a discrete event simulation that enables communication across various independent agents. The system includes different harvesting schedule policies which determine the harvesting order for multiple paddy fields on specific days. The system was evaluated through two distinct experiments: (i) ‘Model Verification Simulation’, which successfully demonstrated the replication of actual historical farming practices, and (ii) ‘Operational Efficiency Simulation’, which compared the overall farm efficiency under different scheduling policies as well as different environmental conditions (e.g., rainfall). The simulation successfully generated a dataset containing traits and performance indicators that replicate the patterns observed in real-world data, while also approximating the operational behaviors and workflows of actual rice harvesting systems. Future studies could further evaluate the model’s robustness to confirm its practical applicability. Full article
(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)
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20 pages, 4074 KiB  
Article
Multi-Agent Reinforcement Symbolic Regression for the Fatigue Life Prediction of Aircraft Landing Gear
by Yi-Pin Sun, Haozhe Feng, Baiyang Zheng, Jiong-Ran Wen, Ai-Fang Chao and Cheng-Wei Fei
Aerospace 2025, 12(8), 718; https://doi.org/10.3390/aerospace12080718 - 12 Aug 2025
Cited by 1 | Viewed by 346
Abstract
Accurate fatigue life prediction of aircraft landing gear is crucial for ensuring flight safety and preventing catastrophic structural failures. However, traditional empirical methods face significant limitations in capturing complex multiaxial loading conditions, while machine learning approaches suffer from lack of interpretability in critical [...] Read more.
Accurate fatigue life prediction of aircraft landing gear is crucial for ensuring flight safety and preventing catastrophic structural failures. However, traditional empirical methods face significant limitations in capturing complex multiaxial loading conditions, while machine learning approaches suffer from lack of interpretability in critical safety applications. To address the dual challenges of prediction accuracy and model interpretability, a multi-agent reinforced symbolic regression (MA-RSR) framework is proposed by integrating multi-agent reinforcement learning with symbolic regression (SR) techniques. Specifically, MA-RSR employs a collaborative mechanism that decomposes complex mathematical expressions into parallel components constructed by independent agents, effectively addressing the search space explosion problem in traditional SR. The system incorporates Transformer-based architecture to enhance symbolic selection capabilities, while an intelligent masking mechanism ensures mathematical rationality through multi-level constraints. To demonstrate effectiveness of the proposed method, validation is conducted using SAE4340 steel multiaxial fatigue data and landing gear finite element simulation. The MA-RSR framework successfully discovers two mathematical expressions achieving R2 of 0.96. Compared to traditional empirical formulas, MA-RSR achieves prediction accuracy improvements exceeding 50% while providing complete interpretability that machine learning methods lack. Furthermore, the multi-agent collaborative mechanism significantly enhances search efficiency through parallel expression construction compared to existing symbolic regression approaches. Full article
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19 pages, 3963 KiB  
Article
Real-Time Energy Management in Microgrids: Integrating T-Cell Optimization, Droop Control, and HIL Validation with OPAL-RT
by Achraf Boukaibat, Nissrine Krami, Youssef Rochdi, Yassir El Bakkali, Mohamed Laamim and Abdelilah Rochd
Energies 2025, 18(15), 4035; https://doi.org/10.3390/en18154035 - 29 Jul 2025
Viewed by 484
Abstract
Modern microgrids face critical challenges in maintaining stability and efficiency due to renewable energy intermittency and dynamic load demands. This paper proposes a novel real-time energy management framework that synergizes a bio-inspired T-Cell optimization algorithm with decentralized voltage-based droop control to address these [...] Read more.
Modern microgrids face critical challenges in maintaining stability and efficiency due to renewable energy intermittency and dynamic load demands. This paper proposes a novel real-time energy management framework that synergizes a bio-inspired T-Cell optimization algorithm with decentralized voltage-based droop control to address these challenges. A JADE-based multi-agent system (MAS) orchestrates coordination between the T-Cell optimizer and edge-level controllers, enabling scalable and fault-tolerant decision-making. The T-Cell algorithm, inspired by adaptive immune system dynamics, optimizes global power distribution through the MAS platform, while droop control ensures local voltage stability via autonomous adjustments by distributed energy resources (DERs). The framework is rigorously validated through Hardware-in-the-Loop (HIL) testing using OPAL-RT, which interfaces MATLAB/Simulink models with Raspberry Pi for real-time communication (MQTT/Modbus protocols). Experimental results demonstrate a 91% reduction in grid dependency, 70% mitigation of voltage fluctuations, and a 93% self-consumption rate, significantly enhancing power quality and resilience. By integrating centralized optimization with decentralized control through MAS coordination, the hybrid approach achieves scalable, self-organizing microgrid operation under variable generation and load conditions. This work advances the practical deployment of adaptive energy management systems, offering a robust solution for sustainable and resilient microgrids. Full article
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19 pages, 650 KiB  
Article
LEMAD: LLM-Empowered Multi-Agent System for Anomaly Detection in Power Grid Services
by Xin Ji, Le Zhang, Wenya Zhang, Fang Peng, Yifan Mao, Xingchuang Liao and Kui Zhang
Electronics 2025, 14(15), 3008; https://doi.org/10.3390/electronics14153008 - 28 Jul 2025
Viewed by 726
Abstract
With the accelerated digital transformation of the power industry, critical infrastructures such as power grids are increasingly migrating to cloud-native architectures, leading to unprecedented growth in service scale and complexity. Traditional operation and maintenance (O&M) methods struggle to meet the demands for real-time [...] Read more.
With the accelerated digital transformation of the power industry, critical infrastructures such as power grids are increasingly migrating to cloud-native architectures, leading to unprecedented growth in service scale and complexity. Traditional operation and maintenance (O&M) methods struggle to meet the demands for real-time monitoring, accuracy, and scalability in such environments. This paper proposes a novel service performance anomaly detection system based on large language models (LLMs) and multi-agent systems (MAS). By integrating the semantic understanding capabilities of LLMs with the distributed collaboration advantages of MAS, we construct a high-precision and robust anomaly detection framework. The system adopts a hierarchical architecture, where lower-layer agents are responsible for tasks such as log parsing and metric monitoring, while an upper-layer coordinating agent performs multimodal feature fusion and global anomaly decision-making. Additionally, the LLM enhances the semantic analysis and causal reasoning capabilities for logs. Experiments conducted on real-world data from the State Grid Corporation of China, covering 1289 service combinations, demonstrate that our proposed system significantly outperforms traditional methods in terms of the F1-score across four platforms, including customer services and grid resources (achieving up to a 10.3% improvement). Notably, the system excels in composite anomaly detection and root cause analysis. This study provides an industrial-grade, scalable, and interpretable solution for intelligent power grid O&M, offering a valuable reference for the practical implementation of AIOps in critical infrastructures. Evaluated on real-world data from the State Grid Corporation of China (SGCC), our system achieves a maximum F1-score of 88.78%, with a precision of 92.16% and recall of 85.63%, outperforming five baseline methods. Full article
(This article belongs to the Special Issue Advanced Techniques for Multi-Agent Systems)
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17 pages, 1327 KiB  
Article
MA-HRL: Multi-Agent Hierarchical Reinforcement Learning for Medical Diagnostic Dialogue Systems
by Xingchuang Liao, Yuchen Qin, Zhimin Fan, Xiaoming Yu, Jingbo Yang, Rongye Shi and Wenjun Wu
Electronics 2025, 14(15), 3001; https://doi.org/10.3390/electronics14153001 - 28 Jul 2025
Viewed by 472
Abstract
Task-oriented medical dialogue systems face two fundamental challenges: the explosion of state-action space caused by numerous diseases and symptoms and the sparsity of informative signals during interactive diagnosis. These issues significantly hinder the accuracy and efficiency of automated clinical reasoning. To address these [...] Read more.
Task-oriented medical dialogue systems face two fundamental challenges: the explosion of state-action space caused by numerous diseases and symptoms and the sparsity of informative signals during interactive diagnosis. These issues significantly hinder the accuracy and efficiency of automated clinical reasoning. To address these problems, we propose MA-HRL, a multi-agent hierarchical reinforcement learning framework that decomposes the diagnostic task into specialized agents. A high-level controller coordinates symptom inquiry via multiple worker agents, each targeting a specific disease group, while a two-tier disease classifier refines diagnostic decisions through hierarchical probability reasoning. To combat sparse rewards, we design an information entropy-based reward function that encourages agents to acquire maximally informative symptoms. Additionally, medical knowledge graphs are integrated to guide decision-making and improve dialogue coherence. Experiments on the SymCat-derived SD dataset demonstrate that MA-HRL achieves substantial improvements over state-of-the-art baselines, including +7.2% diagnosis accuracy, +0.91% symptom hit rate, and +15.94% symptom recognition rate. Ablation studies further verify the effectiveness of each module. This work highlights the potential of hierarchical, knowledge-aware multi-agent systems for interpretable and scalable medical diagnosis. Full article
(This article belongs to the Special Issue Advanced Techniques for Multi-Agent Systems)
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19 pages, 1600 KiB  
Article
A Fixed-Time Convergence Method for Solving Aggregative Games with Malicious Players
by Xuan He, Zhengchao Zeng, Haolong Fu and Zhao Chen
Electronics 2025, 14(15), 2998; https://doi.org/10.3390/electronics14152998 - 28 Jul 2025
Viewed by 261
Abstract
This paper aims to investigate a Nash equilibrium (NE)-seeking approach for the aggregative game problem of second-order multi-agent systems (MAS) with uncontrollable malicious players, which may cause the decisions of global players to become uncontrollable, thereby hindering the ability of normal players to [...] Read more.
This paper aims to investigate a Nash equilibrium (NE)-seeking approach for the aggregative game problem of second-order multi-agent systems (MAS) with uncontrollable malicious players, which may cause the decisions of global players to become uncontrollable, thereby hindering the ability of normal players to reach the NE. To mitigate the influence of malicious players on the system, a malicious player detection and disconnection (MPDD) algorithm is proposed, based on the fixed-time convergence method. Subsequently, a predefined-time distributed NE-seeking algorithm is presented, utilizing a time-varying, time-based generator (TBG) and state-feedback scheme, ensuring that all normal players complete the game problem within the predefined time. The convergence properties of the algorithms are analyzed using Lyapunov stability theory. Theoretically, the aggregative game problem with malicious players can be solved using the proposed algorithms within any user-defined time. Finally, a numerical simulation of electricity market bidding verifies the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Advanced Control Strategies and Applications of Multi-Agent Systems)
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16 pages, 5057 KiB  
Article
Control and Management of Multi-Agent Systems Using Fuzzy Logic for Microgrids
by Zineb Cabrane, Mohammed Ouassaid, Donghee Choi and Soo Hyoung Lee
Batteries 2025, 11(7), 279; https://doi.org/10.3390/batteries11070279 - 21 Jul 2025
Viewed by 350
Abstract
The existing standalone microgrids (MGs) require good energy management systems (EMSs) to respond to energy needs. The EMS presented in this paper is used for an MG based on PV and wind energy sources. The energy storage system is implemented using three packs [...] Read more.
The existing standalone microgrids (MGs) require good energy management systems (EMSs) to respond to energy needs. The EMS presented in this paper is used for an MG based on PV and wind energy sources. The energy storage system is implemented using three packs of batteries. Power smoothing is carried out via the introduction of supercapacitors (SCs) in parallel to the loads and sources. The distribution of energy of the presented MG is focused on the multi-agent system (MAS) using Fuzzy Logic Supervisor control. The MAS is used in order to leverage autonomous and interacting agents to optimize operations and achieve system objectives. To reduce the stress on batteries and avoid damaging all the batteries together by the charge and discharge cycles, one pack of batteries can usually be used. When this pack of batteries is fully discharged and there is a need for energy, it can be taken from another pack of batteries. The same analysis applies to the charge; when batteries of the first pack are fully charged and there is a surplus of energy, it can be stored in other packs of batteries. Two simulation results are used to demonstrate the efficiency of the EMS control used. These simulation tests are proposed with and without SCs. Full article
(This article belongs to the Section Battery Modelling, Simulation, Management and Application)
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24 pages, 1259 KiB  
Article
A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
by Jin Liu, Lei Chen, Zhongbei Tian, Ning Zhao and Clive Roberts
Appl. Sci. 2025, 15(14), 7996; https://doi.org/10.3390/app15147996 - 17 Jul 2025
Viewed by 370
Abstract
Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hindered the scalability [...] Read more.
Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hindered the scalability of these approaches to large-scale systems. This paper proposes a multi-agent system (MAS) that addresses these challenges by decomposing the network into single-junction levels, significantly reducing the search space for real-time rescheduling. The MAS employs a Condorcet voting-based collaborative approach to ensure global feasibility and prevent overly localized optimization by individual junction agents. This decentralized approach enhances both the quality and scalability of train rescheduling solutions. We tested the MAS on a railway network in the UK and compared its performance with the First-Come-First-Served (FCFS) and Timetable Order Enforced (TTOE) routing methods. The computational results show that the MAS significantly outperforms FCFS and TTOE in the tested scenarios, yielding up to a 34.11% increase in network capacity as measured by the defined objective function, thus improving network line capacity. Full article
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20 pages, 1535 KiB  
Article
Multi-Agentic LLMs for Personalizing STEM Texts
by Michael Vaccaro, Mikayla Friday and Arash Zaghi
Appl. Sci. 2025, 15(13), 7579; https://doi.org/10.3390/app15137579 - 6 Jul 2025
Viewed by 739
Abstract
Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and [...] Read more.
Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and a Rewrite agent dynamically adapts science passages via an explicit message-passing protocol. We implement structured system and user prompts as inter-agent communication schemas to enable real-time content adaptation. The results of an ordinal logistic regression analysis hinted that students may be more likely to prefer texts aligned with their profile, demonstrating the feasibility of multi-agent system-driven personalization and highlighting the need for additional work to build upon this pilot study. Beyond empirical validation, we present a modular multi-agent architecture detailing agent roles, communication interfaces, and scalability considerations. We discuss design best practices, ethical safeguards, and pathways for extending this framework to collaborative agent networks—such as feedback-analysis agents—in K-12 settings. These results advance both our theoretical and applied understanding of multi-agent LLM systems for personalized learning. Full article
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19 pages, 2729 KiB  
Article
Physics-Data Fusion Enhanced Virtual Synchronous Generator Control Strategy for Multiple Charging Stations Active Frequency Response
by Leyan Ding, Song Ke, Ghamgeen Izat Rashed, Peixiao Fan and Xingye Shi
World Electr. Veh. J. 2025, 16(7), 347; https://doi.org/10.3390/wevj16070347 - 23 Jun 2025
Viewed by 423
Abstract
In regions where electric vehicles (EVs) are widely adopted and charging stations (CSs) are being built in large numbers, CSs are becoming a critical load-side resource for low-inertia power systems. In this paper, a physics-data fusion enhanced frequency control strategy for multiple CSs [...] Read more.
In regions where electric vehicles (EVs) are widely adopted and charging stations (CSs) are being built in large numbers, CSs are becoming a critical load-side resource for low-inertia power systems. In this paper, a physics-data fusion enhanced frequency control strategy for multiple CSs is proposed. Firstly, the power grid frequency control architecture is improved, where CSs as multi-agent (MA) can participate in frequency response (FR). Besides, a physics-driven adaptive inertia for CS virtual synchronous generators (VSGs) is proposed to improve system dynamic FR characteristics. Building upon this, the physics-data fusion concept is introduced, wherein the MA-soft-actor-critic (MA-SAC) algorithm dynamically adjusts coordination coefficients with the consideration of CSs’ FR capabilities. To validate the proposed strategy, comparative case studies are conducted on the IEEE 39-node system. The simulation results demonstrate that compared to a single physics-driven method, the proposed control strategy exhibits enhanced adaptability and improved FR characteristics across various scenarios. Under intact MA communication conditions, the proposed strategy reduces the frequency disturbance index to 49.872% and the CS response power oscillation index to 79.542%; Even with MA communication impairments, the strategy maintains significant improvements, reducing these indexes to 48.897% and 86.733% respectively. Full article
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26 pages, 1131 KiB  
Article
The Role of Multi-Agent Systems in Realizing Asset Administration Shell Type 3
by Lucas Sakurada, Fernando De la Prieta and Paulo Leitao
Future Internet 2025, 17(7), 270; https://doi.org/10.3390/fi17070270 - 20 Jun 2025
Cited by 2 | Viewed by 1174
Abstract
In the context of Industry 4.0 (I4.0), the Asset Administration Shell (AAS) has been gaining significant attention in recent years. The AAS serves as a standardized digital representation of an asset, encapsulating all relevant information about the asset throughout its lifecycle. Since its [...] Read more.
In the context of Industry 4.0 (I4.0), the Asset Administration Shell (AAS) has been gaining significant attention in recent years. The AAS serves as a standardized digital representation of an asset, encapsulating all relevant information about the asset throughout its lifecycle. Since its introduction in 2015, the past decade has seen considerable progress in developing traditional AAS solutions, namely AAS Type 1 and Type 2. As this initial phase reaches maturity, it becomes essential to shift focus toward AAS Type 3 (proactive), a specific category that extends traditional AAS functionalities by incorporating higher levels of autonomy, intelligence, and collaborative capabilities. However, AAS Type 3 is still in its early stages, lacking formal specifications and comprehensive implementation guidelines. In this context, Multi-Agent Systems (MAS) have been investigated as a means to enhance traditional AAS solutions toward the realization of AAS Type 3, particularly by embedding autonomous, intelligent, and collaborative behaviors. Building on this perspective, this paper explores the role of MAS in realizing AAS Type 3 through a comprehensive analysis of existing agent-based AAS approaches in the literature. Furthermore, this paper proposes a reference model based on common patterns found in the literature to support the development of AAS Type 3 solutions, contributing to the discussion on the formalization of specifications and providing greater clarity on this emerging topic. Finally, to better demonstrate key aspects of the model, some illustrative examples are presented to guide its application and facilitate understanding. Full article
(This article belongs to the Special Issue Artificial Intelligence and Control Systems for Industry 4.0 and 5.0)
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18 pages, 1202 KiB  
Article
Multi-Agent System for Smart Roll-on/Roll-off Terminal Management: Orchestration and Communication Strategies for AI-Driven Optimization
by Nicoletta González-Cancelas, Javier Vaca-Cabrero and Alberto Camarero-Orive
Appl. Sci. 2025, 15(11), 6079; https://doi.org/10.3390/app15116079 - 28 May 2025
Viewed by 778
Abstract
This study presents a structured multi-agent system (MAS) architecture aimed at optimizing operational efficiency in roll-on/roll-off (Ro-Ro) terminal management through intelligent coordination and decentralized decision-making. The proposed framework enhances space allocation, route planning, traffic control, and boarding coordination, enabling real-time decision-making and adaptive [...] Read more.
This study presents a structured multi-agent system (MAS) architecture aimed at optimizing operational efficiency in roll-on/roll-off (Ro-Ro) terminal management through intelligent coordination and decentralized decision-making. The proposed framework enhances space allocation, route planning, traffic control, and boarding coordination, enabling real-time decision-making and adaptive operational strategies. Through structured MAS architecture, agents interact dynamically to optimize vehicle flow, reducing congestion and improving overall efficiency. The study evaluates the system’s potential benefits compared to traditional port management models, highlighting improvements in transit time reduction, resource utilization, and operational resilience. The findings suggest that MAS-based automation can enhance decision-making, sustainability, and integration with Industry 4.0 paradigms, driving the transition toward intelligent, efficient, and scalable port logistics. Full article
(This article belongs to the Special Issue Big-Data-Driven Advances in Smart Maintenance and Industry 4.0)
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24 pages, 3740 KiB  
Article
Distributed Time-Varying Optimal Resource Management for Microgrids via Fixed-Time Multiagent Approach
by Tingting Zhou, Salah Laghrouche and Youcef Ait-Amirat
Energies 2025, 18(10), 2616; https://doi.org/10.3390/en18102616 - 19 May 2025
Viewed by 391
Abstract
This paper investigates the distributed time-varying (TV) resource management problem (RMP) for microgrids (MGs) within a multi-agent system (MAS) framework. A novel fixed-time (FXT) distributed optimization algorithm is proposed, capable of operating over switching communication graphs and handling both local inequality and global [...] Read more.
This paper investigates the distributed time-varying (TV) resource management problem (RMP) for microgrids (MGs) within a multi-agent system (MAS) framework. A novel fixed-time (FXT) distributed optimization algorithm is proposed, capable of operating over switching communication graphs and handling both local inequality and global equality constraints. By incorporating a time-decaying penalty function, the algorithm achieves an FXT consensus on marginal costs and ensures asymptotic convergence to the optimal TV solution of the original RMP. Unlike the prior methods with centralized coordination, the proposed algorithm is fully distributed, scalable, and privacy-preserving, making it suitable for real-time deployment in dynamic MG environments. Rigorous theoretical analysis establishes FXT convergence under both identical and nonidentical Hessian conditions. Simulations on the IEEE 14-bus system validate the algorithm’s superior performance in convergence speed, plug-and-play adaptability, and robustness to switching topologies. Full article
(This article belongs to the Section A1: Smart Grids and Microgrids)
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