Partitioning a pool of capability-heterogeneous human and machine agents into effective teams for multiple concurrent tasks is a fundamental challenge in hybrid human–machine collaboration. We formalize this problem as aMulti-Task Additively Separable Hedonic Game(MT-ASHG), in which every agent is a self-interested player whose utility combines (i) a task-specific proficiency score measuring how well the agent’s skill vector aligns with task requirements, and (ii) an inter-agent compatibility score capturing synergistic or conflicting partnerships. Building on this formulation, we design an Iterative Best Response (IBR) algorithm that lets agents autonomously migrate between task groups to improve their individual payoffs. We prove that the IBR dynamics converge in a finite number of steps to an individually stable partition, in which no agent can unilaterally improve its utility by switching teams, and analyze the computational complexity of the convergence process. To bridge theory and practice, we further introduce an empirical profiling method that extracts capability and compatibility vectors from historical agent interactions, addressing the cold-start problem in newly formed human–machine teams. Extensive experiments on synthetic benchmarks and the Overcooked-AI cooperative environment demonstrate that MT-ASHG consistently outperforms centralized assignment baselines and existing coalition-formation methods in terms of global task completion rate, fairness, and scalability.

In recent years, industrial chain cooperation has evolved into multiplex network structures where product agents are linked through diverse types of interdependencies. While such architectures enhance coordination flexibility, resource-sharing efficiency, and system-level resilience, they also introduce complex hybrid dynamics. These dynamics emerge from fluctuating task demands, continuous changes in network topology as agents join or leave, and variations in production capacities across agents. Their interactions generate cascading cross-layer effects that disrupt load balance and challenge conventional scheduling and resource management strategies. This work addresses the resulting complexity by proposing the hierarchical grouped task migration (HGTM) algorithm, which migrates tasks in groups rather than individually. Leveraging its hierarchical design, HGTM enables effective multilevel load balancing throughout the multiplex structure while keeping computational overhead low. Comprehensive theoretical analysis and experiments show that HGTM enhances task completion rates, improves execution utility, and reduces completion costs. The approach exhibits strong robustness and adaptability, particularly under increasingly dynamic and highly coupled operating conditions.

Industrial software systems underpin complex industrial platforms by integrating cross-disciplinary expertize and sophisticated workflows. Their inherent complexity generates substantial cognitive demands, necessitating contextual, adaptive, and personalized knowledge support aligned with dynamic tasks and evolving expertize. Conventional recommendation approaches struggle to address heterogeneous knowledge sources, dynamic user needs with intent drift, and the domain-specific semantics required in industrial software. To overcome these challenges, we propose a context-aware dialog-based knowledge recommendation (CADKR) model, powered by large language models (LLMs). CADKR fuses dynamic interaction context with domain knowledge through a novel recommendation network (RecNet), enabling robust generalization to unseen scenarios and adaptability to preference drift. For practical deployment, lightweight optimization strategies compress model size by up to 98% without compromising accuracy. A case study in a large chemical industrial park demonstrates the effectiveness of CADKR in enhancing industrial knowledge support, while application deployments in leading new energy vehicle enterprises and the world’s largest circular resource power plant have yielded an independent verification report, providing strong evidence of the proposed method’s practical value.11The report is available athttps://anonymous.4open.science/r/verification-report. Code and data are available athttps://anonymous.4open.science/r/CADKR-TCSS. To meet double-blind requirements, organization and personnel details are anonymized and will be disclosed after acceptance.

In industrial production processes, disruptions within the industrial chain can severely affect the collaborative capabilities of production agents. A notable example occurred during the COVID-19 pandemic, when many agents faced interruption risks and were unable to participate in coordinated production. Ensuring continuity under such conditions requires migrating tasks from disrupted agents to viable alternatives. Designing effective task migration strategies, however, must account for the emergent multiplex nature of modern industrial chains. In these multiplex networked industrial chains, disruption risk in one layer can propagate to others, generating cascading failures across the system. This introduces two key challenges: (1) disruption risk creates mismatches not only between product agents and tasks but also across network layers, enlarging the problem dimensionality; and (2) simultaneous disruptions across multiple agents and layers increase the volume of tasks needing migration, greatly expanding the solution space. To address these challenges, we introduce the notion of a multiplex potential field, which captures cross-layer interdependencies and system-level dynamics in multiplex industrial chains. Building on this concept, we develop a hierarchical contextual task migration algorithm that exploits the multiplex potential field to guide both inter-layer and intra-layer task reallocations. Extensive experiments show that our approach consistently achieves superior utility, markedly improves task completion ratios, and reduces execution costs compared to benchmark algorithms. Furthermore, it attains solution quality comparable to that of the optimal CPLEX solver while requiring substantially less computation time. Finally, a case study on the FAO international food trade network demonstrates that the proposed framework is not only theoretically robust but also practically effective when deployed on large-scale real-world multiplex systems.

In the heterogeneous and dynamically evolving Industrial Internet, industrial agents are required to possess cross-domain adaptability and self-learning capabilities to facilitate task generalization and scalable deployment across diverse operational contexts. However, existing domain adaptation approaches predominantly rely on static feature alignment or domain-invariant assumptions, lacking a systematic consideration of working condition variability and the interplay between self-learning and adaptation. This oversight hampers their effectiveness in real-world industrial scenarios, where agents must operate under complex conditions with limited target domain knowledge. Consequently, these methods often suffer from knowledge shift and insufficient policy generalization. To address these limitations, this article introduces the instance weighting-based domain-adaptive optimization (IW-DAO) framework. IW-DAO combines an instance weighting-based knowledge alignment mechanism with a Bayesian optimization strategy, forming a dynamic self-learning loop tailored for cross-domain adaptation. Specifically, the framework constructs an adaptive knowledge representation in a high-dimensional invariant feature space and formulates a cross-domain performance evaluation estimator to guide the unsupervised learning of knowledge transfer and adaptive optimization via Bayesian iterative search. Extensive experiments on industrial asset management tasks as well as a real-world industrial flow process dataset with various operating conditions demonstrate the effectiveness of IW-DAO. The proposed framework enables industrial agents to evolve autonomously and be deployed efficiently across diverse domains. IW-DAO consistently outperforms baseline and expert-tuned methods, demonstrating strong generalization and adaptability in both industrial asset management and complex flow process scenarios.

Path planning is an important step in ensuring the safety of unmanned surface vehicle (USV) navigation and executing missions quickly and efficiently. However, current USV path planning methods lack comprehensive consideration of electronic nautical charts and meteorological data, resulting in planned paths being unable to fully utilize marine environmental conditions, which may easily lead to collisions and long navigation times. Based on the above considerations, our study designs a USV path planning system that comprehensively considers the multimodal information from electronic nautical charts and meteorological data. The system consists of three parts: 1) the image processing module; 2) the meteorological analysis module; and 3) the path planning module. In detail, the image processing module obtains the geographical feature information from the electronic chart and constructs a static obstacle environment. The meteorological analysis module obtains the meteorological feature information from meteorological data and constructs a dynamic meteorological vector field environment. The path planning module introduces a designed double deep Q-Network (DQN) structure, a multivariate weighted Dueling network, and a priority sampling mechanism to enhance the DQN algorithm for promising performance in USV path planning. Extensive experiments illustrate the superior performance of the proposed fusion DQN algorithm. Furthermore, the feasibility of the entire path planning system is confirmed.

In the context of establishing emission control areas (ECAs) in many ports to meet the challenges posed by air pollution, the use of drone-carrying sniffers to perform emission monitoring missions has become a new monitoring mode for ECAs. The operational management problem of drones in ECAs, namely, drone scheduling problem (DSP), is eliciting the attention of researchers. To consider the influence of vessel traffic on the demand for drones, this study proposes a rental-based drone operation model. In the model, the number of drones used depends on the load of monitoring missions. Maximizing the cumulative monitoring reward and minimizing the use number of drones within the minimum monitoring rate constraint are used as optimization objectives to maximize the cost return of the rental-based operation model. The rental-based drone operation model is modeled as a multi-objective DSP (MDSP). Furthermore, we horizontally compare the characteristics of MDSP with those of many classical models in the field of operations research. Afterward, we reveal the similarities and differences between MDSP and previous models. We find that MDSP has the non-first-in-first-out property, whereas most of the advanced models have the first-in-first-out property, which leads to the failure of the developed efficient algorithms in solving MDSP. Therefore, this study innovatively designs four feasible multi-objective optimization methods for MDSP. Numerical experiments are conducted to evaluate the performance of the four methods in solving MDSP with different scales. In terms of theoretical implications, experimental results prove that the proposed methods for solving MDSP are feasible and effective. In terms of practical implications, the proposed rental-based vessel monitoring operation model shows great potential for practical engineering.

The drone has become a promising tool to improve the efficiency of vessel emission monitoring in emission control areas of the part due to its high mobility. However, how to optimize the flight path of drones to improve the weighted sum of monitored vessels, i.e., drone scheduling problem (DSP), is a not yet fully researched problem. In this paper, different from the classic optimization solution method used by the literature, an efficient ant colony-based algorithm is developed to solve DSP. Given the characteristics of DSP, a hierarchical-based pheromone update strategy and partition-based pheromone management mechanism are proposed to optimize the typical ant colony algorithm. Numerical experiments not only illustrate the feasibility of using the ant colony algorithm to solve DSP, but also show that the algorithm we proposed outperforms other compared methods in terms of the solution quality and the solving speed under different problem scales.

How to effectively organize drones to monitor pollutants from vessels is an important operational problem in port management. It is defined as the drone scheduling problem (DSP). The effectiveness of precise algorithms and heuristic algorithms in solving DSP has been reported in previous studies. In previous studies, the speed of the vessel was assumed to be constant. However, since the influence of sea waves and vessel power, such an assumption is difficult to satisfy in actual scenarios. The actual position of the vessel may deviate from the position information obtained through prior calculations. As the cumulative position deviation increases, it is possible to make the original feasible monitoring scheme infeasible. It is necessary to consider the emission monitoring dispatching of drones under vessel speed fluctuation in actual monitoring activities of the vessel. To deal with the problem, a dynamic dispatching strategy based on reinforcement learning (RL) is proposed. Considering the vessel speed fluctuation, the monitoring window is divided into multiple sub-time windows. The route information of the vessel in each sub-time window is updated according to the vessel speed fluctuations to reduce the accumulation of deviations between the prior position and the actual position. Then, a lightweight RL strategy is adopted to quickly (re)organize the monitoring scheme in each sub-time window. Numerical experiments illustrate the above division-conquer approach could effectively reduce the possibility of drone monitoring failure caused by vessel speed fluctuations. Also, the superiority of the RL-based dispatching strategy is illustrated by comparing it with multiple dispatching schemes.