The rise of multi-industry chain networks has transformed traditional industrial operations into complex systems with intricate cross-layer resource dependencies and company group structures. Despite enhancing operational efficiency, these networks are increasingly vulnerable to task surges—sudden spikes in resource demand that propagate across layers, and trigger cascading failures. Existing emergency response strategies, designed for single-layer networks, fail to address the multidimensional challenges of maintaining critical company stability and ensuring overall network robustness. To address this gap, this paper introduces the Multi-Modal Collaborative (MMCol) algorithm, which incorporates: (1) A dual-dimension collaboration framework enabling coordination across intra-layer vs. inter-layer and single company vs. company group, (2) adaptive inter-layer collaboration coefficients for dynamic resource allocation and stability preservation, and (3) a hierarchical decision strategy that reduces computational complexity by treating company groups as collaborative units and by pre-selecting layers based on resource compatibility. Extensive simulations across various network scales and experiments on real industry chain datasets demonstrate that MMCol consistently outperforms state-of-the-art algorithms in key metrics including load balance, collaboration cost, and key entity risk protection. The performance advantages are particularly significant in complex networks that mirror real industrial structures, thereby confirming MMCol’s effectiveness in practical multi-industry chain environments.

With the advancement of electronic information technology and the growth of the intelligent industry, the industrial sector has undergone a shift from simplex, linear, and vertical chains to complex, multi-level, and multi-dimensional networked industrial chains. In order to enhance energy efficiency in multiplex networked industrial chains under time-of-use price, a coarse time granularity task scheduling approach has been adopted. This approach adjusts the distribution of electricity supply based on task deadlines, dividing it into longer periods to facilitate batch access to task information. However, traditional simplex-network task assignment optimization methods are unable to achieve a globally optimal solution for cross-layer links in multiplex networked industrial chains. Existing solutions struggle to balance execution costs and completion efficiency in time-of-use price scenarios. Therefore, this paper presents a mixed-integer linear programming model for solving the problem scenario and two algorithms: an exact algorithm based on the branch-and-bound method and a multi-objective heuristic algorithm based on cross-layer policy propagation. These algorithms are designed to adapt to small-scale and large-scale problem scenarios under coarse time granularity. Through extensive simulation experiments and theoretical analysis, the proposed methods effectively optimize the energy and time costs associated with the task execution.

Recently, with the increasing complexity of multiplex Unmanned Aerial Vehicles (multi-UAVs) collaboration in dynamic task environments, multi-UAVs systems have shown new characteristics of inter-coupling among multiplex groups and intra-correlation within groups. However, previous studies often overlooked the structural impact of dynamic risks on agents among multiplex UAV groups, which is a critical issue for modern multi-UAVs communication to address. To address this problem, we integrate the influence of dynamic risks on agents among multiplex UAV group structures into a multi-UAVs task migration problem and formulate it as a partially observable Markov game. We then propose a Hybrid Attention Multi-agent Reinforcement Learning (HAMRL) algorithm, which uses attention structures to learn the dynamic characteristics of the task environment, and it integrates hybrid attention mechanisms to establish efficient intra- and inter-group communication aggregation for information extraction and group collaboration. Experimental results show that in this comprehensive and challenging model, our algorithm significantly outperforms state-of-the-art algorithms in terms of convergence speed and algorithm performance due to the rational design of communication mechanisms.