Search Results (32)

As the scale of distributed resources continues to expand, decentralization and multi-agent characteristics bring significant challenges to low-carbon dispatching and market participation of power grids. To this end, this paper proposes a collaborative optimization scheduling framework with distributed resource aggregators (DRAs) as the main body, innovatively coupling carbon Emission trading (CET) with electric vehicle carbon quota participation, and the renewable energy quota (RPS) with tradable green certificate (TGC) transaction as the carrier, as well as constructing the connection path between the two to realize the integrated utilization of environmental rights and interests. Based on the ε-constraint method, a bi-objective optimization model of economic cost minimization and carbon emission minimization is established, and a multi-dimensional evaluation system, covering the internal and overall operation performance of the aggregator, is designed. The example shows that, under the proposed CET-RPS coupling mechanism, the total cost of DRA is about 23.4% lower than that of the existing mechanism. When the carbon emission constraint is relaxed from 2700 t to 3000 t, the total cost decreases from CNY 2537.32 to CNY 2487.74, indicating that the carbon constraint has a significant impact on the marginal cost. This study provides a feasible path for the large-scale participation of distributed resources in low-carbon power systems. Full article

In the highly competitive manufacturing environment, customers are increasingly demanding punctual, flexible, and customized deliveries, compelling enterprises to balance profit, energy efficiency, and production performance while seeking new scheduling methods to enhance dynamic responsiveness. Although deep reinforcement learning (DRL) has made progress in dynamic flexible job shop scheduling, existing research has rarely addressed profit-oriented optimization. To tackle this challenge, this paper proposes a novel multi-objective dynamic flexible job shop scheduling (MODFJSP) model that aims to maximize net profit and minimize makespan on the basis of traditional FJSP. The model incorporates uncertainties such as new job insertions, fluctuating due dates, and high-profit urgent jobs, and establishes a multi-agent collaborative framework consisting of “job selection–machine assignment.” For the two types of agents, this paper proposes adaptive state representations, reward functions, and variable action spaces to achieve the dual optimization objectives. The experimental results show that the double deep Q-network (DDQN), within the multi-agent cooperative framework, outperforms PPO, DQN, and classical scheduling rules in terms of solution quality and robustness. It achieves superior performance on multiple metrics such as IGD, HV, and SC, and generates bi-objective Pareto frontiers that are closer to the ideal point. The results demonstrate the effectiveness and practical value of the proposed collaborative framework for solving MODFJSP. Full article

The rapid growth of Industrial Internet of Things (IIoT) terminals has resulted in tasks exhibiting increased concurrency, heterogeneous resource demands, and dynamic priorities, significantly increasing the complexity of task scheduling in edge computing. Cloud–edge–end collaborative computing leverages cross-layer task offloading to alleviate edge node resource contention and improve task scheduling efficiency. However, existing methods generally neglect the joint optimization of task offloading, resource allocation, and priority adaptation, making it difficult to balance task execution and resource utilization under resource-constrained and competitive conditions. To address this, this paper proposes a two-stage dynamic-priority-aware joint task offloading and resource allocation method (DPTORA). In the first stage, an improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm integrated with a Priority-Gated Attention Module (PGAM) enhances the robustness and accuracy of offloading strategies under dynamic priorities; in the second stage, the resource allocation problem is formulated as a single-objective convex optimization task and solved globally using the Lagrangian dual method. Simulation results show that DPTORA significantly outperforms existing multi-agent reinforcement learning baselines in terms of task latency, energy consumption, and the task completion rate. Full article

This study explores the integrated scheduling optimization of coal port operations, addressing the dual challenges of handling efficiency and resource conservation by coordinating equipment scheduling with stockyard spraying operations. Through a systematic analysis of operational processes in coal ports, a mixed-integer linear programming (MILP) model is developed to achieve global optimization while explicitly quantifying water and electricity consumption in spraying operations. To address this complex problem, we propose a novel hybrid algorithm that integrates a micro-evolutionary algorithm (MEA) framework with multi-agent proximal policy optimization (MAPPO), enabling adaptive decision-making for large-scale real-time scheduling. Three specialized agents for crossover, mutation, and neighborhood search achieve collaborative optimization by observing population features as states, selecting evolutionary operators as actions, and receiving composite rewards based on both population improvement and individual contributions. This strategy facilitates adaptive operator selection and optimal evolutionary direction derivation, collectively guiding population evolution toward high-quality solutions. Extensive experiments on ten scaled instances of a real-world coal port confirm the proposed algorithm’s superior performance. Compared with four other standard algorithms, it consistently yields higher hypervolume (HV) values and lower inverted generational distance (IGD) metrics, which collectively demonstrate stronger convergence capability and higher solution quality. Full article

To enhance the overall operational efficiency of heavy haul railway port stations, which serve as critical hubs in rail–water intermodal transportation systems, this study develops a novel scheduling optimization method that integrates operation plans and resource allocation. By analyzing the operational processes of heavy haul trains and shunting operation modes within a hybrid unloading system, we establish an integrated scheduling optimization model. To solve the model efficiently, a dual-agent advantage actor–critic with Pareto reward shaping (DAA2C-PRS) algorithm framework is proposed, which captures the matching relationship between operations and resources through joint actions taken by the train agent and the shunting agent to depict the scheduling decision process. Convolutional neural networks (CNNs) are employed to extract features from a multi-channel matrix containing real-time scheduling data. Considering the objective function and resource allocation with capacity, we design knowledge-based composite dispatching rules. Regarding the communication among agents, a shared experience replay buffer and Pareto reward shaping mechanism are implemented to enhance the level of strategic collaboration and learning efficiency. Based on this algorithm framework, we conduct experimental verification at H port station, and the results demonstrate that the proposed algorithm exhibits a superior solution quality and convergence performance compared with other methods for all tested instances. Full article

Large language models (LLMs) have demonstrated impressive capabilities, yet they face significant limitations in real-world applications. To overcome these boundaries, research areas such as tool learning, model collaboration, agents, and multi-agent systems have increasingly drawn attention. However, current studies are often conducted in isolation, lacking a unified framework for systematic integration, which hinders the synergy among closely related research efforts. To address this gap, this study, for the first time, brings together tool learning, model collaboration, and agent-related fields under a unified framework based on the concepts of the “Big Loop” and “Atomization”. In this framework, atomic components refer to fundamental units such as models, tools, and agents. The Big Loop is formed through interactions among these atomic components to achieve end-to-end task completion—for example, tool calling requires the integration of agent modules, tool retrieval models, and tools themselves; multi-agent systems require coordination among multiple agent units. This review first clarifies the foundational concepts of the Big Loop and Atomization and elaborates on the advantages of the Big Loop compared to a single LLM. It then systematically introduces the construction of atomic components, the scheduling of these components within the Big Loop, and the optimization of the overall system. The paper also discusses existing challenges and outlines future research directions. This work aims to offer a systematic perspective for both academia and industry, and to chart a course for exploration in this emerging and highly promising field of Big Loop and Atomization. Full article

Port seaside scheduling, involving berth allocation, quay crane, and tugboat scheduling, is central to intelligent port operations. This survey reviews and statistically analyzes 152 academic publications from 2000 to 2025 that focus on optimization techniques for port seaside scheduling. The reviewed methods span mathematical modeling and exact algorithms, heuristic and simulation-based approaches, and agent-based and learning-driven techniques. Findings show deterministic models remain mainstream (77% of studies), with uncertainty-aware models accounting for 23%. Heuristic and simulation approaches are most commonly used (60.5%), followed by exact algorithms (21.7%) and agent-based methods (12.5%). While berth and quay crane scheduling have historically been the primary focus, there is growing research interest in tugboat operations, pilot assignment, and vessel routing under navigational constraints. The review traces a clear evolution from static, single-resource optimization to dynamic, multi-resource coordination enabled by intelligent modeling. Finally, emerging trends such as the integration of large language models, green scheduling strategies, and human–machine collaboration are discussed, providing insights and directions for future research and practical implementations. Full article

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization. Full article

As global energy systems transition towards greater reliance on renewable energy sources, the integration of energy storage systems (ESSs) becomes increasingly critical to managing the intermittency and variability associated with renewable generation. This paper provides a comprehensive review of the application of evolutionary game theory (EGT) to optimize ESSs, emphasizing its role in enhancing decision-making processes, operation scheduling, and multi-agent coordination within dynamic, decentralized energy environments. A significant contribution of this paper is the incorporation of negotiation mechanisms and collaborative decision-making frameworks, which are essential for effective multi-agent coordination in complex systems. Unlike traditional game-theoretic models, EGT accounts for bounded rationality and strategic adaptation, offering a robust tool for modeling the interactions among stakeholders such as energy producers, consumers, and storage operators. The paper first addresses the key challenges in integrating ESS into modern power grids, particularly with high penetration of intermittent renewable energy. It then introduces the foundational principles of EGT and compares its advantages over classical game theory in capturing the evolving strategies of agents within these complex environments. A key innovation explored in this review is the hybridization of game-theoretic models, combining the stability of classical game theory with the adaptability of EGT, providing a comprehensive approach to resource allocation and coordination. Furthermore, this paper highlights the importance of deliberative democracy and process-based negotiation decision-making mechanisms in optimizing ESS operations, proposing a shift towards more inclusive, transparent, and consensus-driven decision-making. The review also examines several case studies where EGT has been successfully applied to optimize both local and large-scale ESSs, demonstrating its potential to enhance system efficiency, reduce operational costs, and improve reliability. Additionally, hybrid models incorporating evolutionary algorithms and particle swarm optimization have shown superior performance compared to traditional methods. The future directions for EGT in ESS optimization are discussed, emphasizing the integration of artificial intelligence, quantum computing, and blockchain technologies to address current challenges such as data scarcity, computational complexity, and scalability. These interdisciplinary innovations are expected to drive the development of more resilient, efficient, and flexible energy systems capable of supporting a decarbonized energy future. Full article

Background: The rapid growth of international maritime trade has intensified operational challenges at marine terminals due to increased interaction between vessels, trucks, and trains. Key issues include berth congestion, inefficient truck arrivals, and underutilization of terminal resources. Ensuring coordinated planning among transport modes and fostering collaboration between stakeholders such as vessel operators, logistics providers, and terminal managers is critical to mitigating these inefficiencies. Methods: This study proposes a multi-agent, multi-objective coordination model that synchronizes vessel berth allocation with truck appointment scheduling. A solution method combining prioritized planning with a neighborhood search heuristic is introduced to explore Pareto-optimal trade-offs. The performance of this approach is benchmarked against well-established multi-objective evolutionary algorithms (MOEAs), including NSGA-II and SPEA2. Results: Numerical experiments demonstrate that the proposed method generates a greater number of Pareto-optimal solutions and achieves higher hypervolume indicators compared to MOEAs. These results show improved balance among objectives such as minimizing vessel waiting times, reducing truck congestion, and optimizing terminal resource usage. Conclusions: By integrating berth allocation and truck scheduling through a transparent, multi-agent approach, this work provides decision-makers with better tools to evaluate trade-offs in port terminal operations. The proposed strategy supports more efficient, fair, and informed coordination in complex multimodal environments. Full article

With the rapid advancement of space technology, satellites are playing an increasingly vital role in fields such as Earth observation, communication and navigation, space exploration, and military applications. Efficiently deploying satellite missions under multi-objective, multi-constraint, and dynamic environments has become a critical challenge in the current aerospace domain. This paper integrates the concepts of game theory and proposes a distributed collaborative task model suitable for on-orbit satellite mission planning. A two-player impulsive maneuver game model is constructed using differential game theory. Based on the ideas of Nash equilibrium and distributed collaboration, multi-agent technology is applied to the distributed collaborative task planning, achieving collaborative allocation and countermeasure strategies for multi-objective and multi-satellite scenarios. Experimental results demonstrate that the method proposed in this paper exhibits good adaptability and robustness in multiple impulse scheduling, maneuver strategy iteration, and heterogeneous resource utilization, providing a feasible technical approach for mission planning and game confrontation in satellite clusters. Full article

Complex scenario analysis and evaluation simulations often involve multiple sets of simulation applications with different combinations of parameters, thus resulting in high computing power consumption, which is one of the factors that limits the efficiency of multi-sample parallel simulations. Cloud computing provides considerable amounts of cheap and convenient computing resources, thus providing efficient support for multi-sample simulation tasks. However, traditional simulation scheduling methods do not consider the collaborative parallel scheduling of multiple samples and multiple entities under multi-objective constraints. Deep reinforcement learning methods can continuously learn and adjust their strategies through interactions with the environment, demonstrating strong adaptability in response to dynamically changing task requirements. Therefore, herein, a parallel simulation multi-sample task scheduling method based on deep reinforcement learning in a cloud computing environment is proposed. The method collects cluster load information and simulation application information as state inputs in the cloud environment, designs a multi-objective reward function to balance the cost and execution efficiency, and uses deep Q-networks (DQNs) to train agents for intelligent scheduling of multi-sample parallel simulation tasks. In a real cloud environment, the proposed method demonstrates runtime reductions of 4–11% and execution cost savings of 11–22% compared to the Round-Robin algorithm, Best Fit algorithm, and genetic algorithm. Full article

Background: With advancements in Generative Artificial Intelligence, various industries have made substantial efforts to integrate this technology to enhance the efficiency and effectiveness of existing processes or identify potential weaknesses. Context, however, remains a crucial factor in leveraging intelligence, especially in high-stakes sectors such as healthcare, where contextual understanding can lead to life-changing outcomes. Objective: This research aims to develop a practical medical multi-agent system framework capable of automating appointment scheduling and triage classification, thus improving operational efficiency in healthcare settings. Methods: We present MedScrubCrew, a multi-agent framework integrating established technologies: Gale-Shapley stable matching algorithm for optimal patient-provider allocation, knowledge graphs for semantic compatibility profiling, and specialized large language model-based agents. The framework is designed to emulate the collaborative decision making processes typical of medical teams. Results: Our evaluation demonstrates that combining these components within a cohesive multi-agent architecture substantially enhances operational efficiency, task completeness, and contextual relevance in healthcare scheduling workflows. Conclusions:MedScrubCrew provides a practical, implementable blueprint for healthcare automation, addressing significant inefficiencies in real-world appointment scheduling and patient triage scenarios. Full article

Maritime vision sensing can be applied to maritime unmanned systems to perform search and rescue (SAR) missions under complex marine environments, as multiple unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are able to conduct vision sensing through the air, the water-surface, and underwater. However, in these vision-based maritime SAR systems, collaboration between UAVs and USVs is a critical issue for successful SAR operations. To address this challenge, in this paper, we propose a long-endurance collaborative SAR scheme which exploits the complementary strengths of the maritime unmanned systems. In this scheme, a swarm of UAVs leverages a multi-agent reinforcement-learning (MARL) method and probability maps to perform cooperative first-phase search exploiting UAV’s high altitude and wide field of view of vision sensing. Then, multiple USVs conduct precise real-time second-phase operations by refining the probabilistic map. To deal with the energy constraints of UAVs and perform long-endurance collaborative SAR missions, a multi-USV charging scheduling method is proposed based on MARL to prolong the UAVs’ flight time. Through extensive simulations, the experimental results verified the effectiveness of the proposed scheme and long-endurance search capabilities. Full article

Time-triggered Ethernet combines time-triggered and event-triggered communication, and is suitable for fields with high real-time requirements. Aiming at the problem that the traditional scheduling algorithm is not effective in scheduling event-triggered messages, a message scheduling algorithm based on multi-agent reinforcement learning (MADDPG, Multi-Agent Deep Deterministic Policy Gradient) and a hybrid algorithm combining SMT (Satisfiability Modulo Theories) solver and MADDPG are proposed. This method aims to optimize the scheduling of event-triggered messages while maintaining the uniformity of time-triggered message scheduling, providing more time slots for event-triggered messages, and reducing their waiting time and end-to-end delay. Through the designed scheduling software, in the experiment, compared with the SMT-based algorithm and the traditional DQN (Deep Q-Network) algorithm, the new method shows better load balance and lower message jitter, and it is verified in the OPNET simulation environment that it can effectively reduce the delay of event-triggered messages. Full article