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.

The rapid expansion of the satellite industry has presented numerous opportunities across various sectors and significantly transformed people's daily lives. However, the high energy consumption resulting from frequent task execution poses challenges for satellite management. Energy consumption has become an important factor to be considered in the design of future satellite management systems. The energy-efficient satellite range scheduling problem (EESRSP) aims to optimize task sequencing profits within the satellite management system while simultaneously conserving energy. To address this problem, a mixed-integer scheduling model is constructed, taking into account the energy consumption of ground stations during telemetry, tracking and command (TT&C) operations. Then, we propose a reinforcement learning-based memetic algorithm (RL-MA) that incorporates a heuristic initialization method (HIM). The HIM enables the algorithm to rapidly generate high-quality initial solutions by leveraging task features associated with EESRSRP. RL-MA employs both population search and local search techniques to explore the satellite TT&C task plan. RL-MA incorporates two genetic operators, crossover and mutation, into the population-based search. In the local search stage, multiple random and heuristic local search operators are incorporated through an ensemble local search strategy (ELSS). To improve search performance, Q-learning, a classical class of reinforcement learning (RL) methods tailored to problem characteristics, is utilized for selecting effective operators. RL dynamically adjusts local search operators based on strategy performance. Experimental results demonstrate that the proposed RL-MA can effectively generate sound solutions for EESRSP with varying task scales. Furthermore, the improvement strategies employed in the algorithm are validated to enhance the scheduling performance of RL-MA. This study reveals that integrating RL with an ensemble of local search operators can significantly enhance the algorithm's exploit capability. Moreover, this local search approach applies to solving other types of satellite scheduling problems.

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.

Aviation networks consist of networks of flight services provided by numerous airlines and are represented in the form of multiplex networks composed of a set of nodes, multiple layers of links, and coupling node relationships across all layers. However, multiplex aviation networks (MANs) are vulnerable to disturbances due to potential cascading failures. Thus, the robustness of MANs must be maintained. Previous studies on the robustness of MANs have mainly focused on the pairwise interactions between two nodes, which are insufficient for characterizing the dynamic processes of actual MANs. In addition, current cascading failure models are not adequate for MANs, as flow must be redistributed within multiplex networks rather than to nearby airports. To solve these issues, this study developed a topology model of MANs and introduced a model of node congestion to simulate the cascading failure process. Given the robustness assessment of MANs under intentional attacks, numerous analyses of higher-order interactions in networks are conducted. A higher-order cycle structure rewiring strategy is proposed to enhance the dynamic interaction among the layers and further improve the robustness of the MANs. Extensive experiments on synthetic and actual EU-Air multiplex networks are presented to illustrate the superiority of the proposed approach over state-of-the-art algorithms in improving the robustness of MANs.

The tradeoff of effectiveness and cost is a vital problem for complex industrial systems, mainly applied in the weapons and aviation fields. As a typical complex industrial system, the effectiveness-cost tradeoff of the satellites becomes challenging and interesting. This paper takes a remote sensing satellite as a research object, and an integrated approach to assess and optimize its effectiveness and cost is proposed. The characteristic parameters are selected according to an analysis of its structure and mission. Furthermore, the effectiveness evaluation model is established based on the Availability-Dependability-Capability (ADC) model, and the cost parameter model is developed using historical data and regression analysis. According to the Cost as Independent Variable (CAIV) method, the objective function of the satellite effectiveness-cost with the effectiveness-cost tradeoff space is established. The objective function is solved and optimized using a genetic algorithm to achieve a more efficient and economical satellite design scheme.