This paper studies the network dismantling (ND) problem and aims to develop more effective models and approaches to cope with it, such that a given network can be dismantled by a set of vital nodes of minimum size. To achieve that, we propose a three-phase framework—the Percolation coarsening, Belief propagation dismantling, and Fragmentation optimization fine-tuning (PBF) framework—consisting of PBF-I, PBF-II, and PBF-III, where we contribute three new and one improved algorithms. In particular, PBF-I studies strategies to effectively coarsen the studied network via the merger of less influential nodes, such that the computational efficiency of the follow-up PBF-II phase can be maximized. PBF-II considers the superiority of the belief propagation (BP) algorithm in the ND problem and proposes an improved BP to identify vital nodes from the coarse-grained network, which particularly focuses on the largest connected component and obtains the vital nodes from a filtered candidate set. In addition, PBF-III presents fine-tuning strategies to further improve the quality of solutions obtained in PBF-II. The effectiveness of the proposed framework is validated on over 10 empirical networks in regard to varied circumstances. Our results show that the developed framework can obtain dismantling node sets of much smaller sizes compared to the state-of-the-art in almost all cases. Meanwhile, our framework is also more effective, efficient, and stable compared to existing methods, and is capable of tackling the ND problem in extremely large networks. We are convinced that the model and methodology introduced in this paper could be applied to many applications, such as the robustness and resilience analysis of network-structural infrastructures, the suppression of epidemics, and the containment of misinformation on social networks. The source code of the proposed PBF framework will be made publicly available upon acceptance of the manuscript.

Timetabling and scheduling of electric buses (EBs) are crucial for a sustainable city transportation system. Most studies mainly focus on the scheduling and timetabling for one EB line based on the assumption that energy consumption of a battery is linear with distance. In this study, timetabling and scheduling for multiple EB lines are investigated by considering nonlinear energy consumption due to the dynamic load of buses. The optimization objectives are to minimize the number of vehicles and total operation costs, in which the constraints include the limitations of operation range and nonlinear energy consumption of the battery and the capacity of buses can be simultaneously charged at charging stations. Further, an improved particle swarm optimization algorithm is developed to obtain the optimal solution set. Compared with the existing schedule, the proposed schedule can not only reduce the number of vehicles and total charging costs but also distribute the charging periods during off-peak hours to reduce the impact on the urban electricity grid.

The utilization of multiple unmanned aerial vehicles (multi-UAVs) greatly enhances the quality of public services, whereas presents challenges to effective mission planning. Existing research predominantly concentrates on task allocation and path planning, with limited emphasis on integrating UAV deployment and task allocation. Despite the promise of learning-based approaches for joint optimization, their applicability to sim-to-real problems remains restricted. In this paper, we propose a hierarchical decision-making framework empowered by digital twin (DT) technology to enhance resource-efficient utilization and facilitate realtime mission planning. In the upper layer, a swarm of UAVs is divided into subgroups using a K-means method and strategically deployed to corresponding task regions. In the lower layer, a DT-enabled reinforcement learning approach is adopted to leverage real-world experiences. Specifically, we employ a multiagent deep Q learning algorithm to optimize the training process, where each agent interacts with the DT environment through original and target networks to acquire an optimal strategy. The proposed approach is evaluated using simulations that demonstrate its effectiveness in achieving high-quality plans.

Effective recovery after disruptions is essential to improve system resilience. For many distributed systems such as offshore wind farms and communication networks, spatial location characteristics and limited maintenance resources lead to discontinuous changes in performance during system recovery. However, the stepwise performance is frequently ignored. The coupling relationship between the system and maintenance teams makes it difficult to improve the system resilience from the recovery perspective. In this article, a new method that combines the resilience importance measure and enhanced pigeon-inspired optimization (PIO) is presented to optimize the system resilience under stepwise recovery conditions. First, this study proposes a resilience-oriented importance measure that evaluates the recovery priority for each component. Second, an optimization model is established to minimize the system resilience loss by joint optimization of recovery sequence and task assignment under the conditions of multiple maintenance teams. Third, the resilience importance measure is combined with roulette wheel selection to form a high-quality intitle swarm of PIO. Fourth, an importance measure-based PIO with a Gaussian mutation and adaptive crossover operator is designed to find an optimization solution for system resilience. Finally, the recovery of a network system consisting of 32 nodes and 71 edges is studied. Compared with the stochastic method and traditional PIO, the proposed method in this study reduces the system resilience loss by 48.87 and 15.54%, respectively.