Content distribution in vehicular ad hoc networks (VANET) plays an important role to achieve both safety and non-safety types of services. A high-quality scheduling scheme for content distribution ...can improve transmission efficiency. In this context, we propose a data transmission scheduling approach named data transmission scheduling considering broken-point continuingly-transferring technique (DTS-BPCT) for content distribution in VANETs. Based on the centralized control mode and BPCT technique, files can be split into multiple parts and transmitted via multiple relay nodes. An integer programming model is formulated to describe the scheduling problem and a corresponding heuristic approach in which the content is scheduled by two stages is developed to solve the problem, thus generating a high-quality scheduling scheme to reduce duplicate transmissions. In addition, to make the proposed scheduling approach being applicable to real-world scenarios, relay nodes selection, available time windows generation, and requests sorting strategies are presented and properly incorporated into the scheduling approach. Finally, comparison studies show that the proposed algorithm is superior to the First Come First Serve and Smallest Data Size First in terms of the transmission success rate and network throughput.
Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a ...high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales.
Earth observation satellite (EOS) systems often encounter emergency observation tasks oriented to sudden disasters (e.g., earthquake, tsunami, and mud-rock flow). However, EOS systems may not be able ...to provide feasible coverage time windows for emergencies, which requires that an appropriately selected satellite transfers its orbit for better observation. In this context, we investigate the orbit maneuver optimization problem. First, by analyzing the orbit coverage and dynamics, we construct three models for describing the orbit maneuver optimization problem. These models, respectively, consider the response time, ground resolution, and fuel consumption as optimization objectives to satisfy diverse user requirements. Second, we employ an adaptive differential evolution (DE) integrating ant colony optimization (ACO) to solve the optimization models, which is named ACODE. In ACODE, key components (i.e., genetic operations and control parameters) of DE are formed into a directed acyclic graph and an ACO is appropriately embedded into an algorithm framework to find reasonable combinations of the components from the graph. Third, we conduct extensive experimental studies to show the superiority of ACODE. Compared with three existing algorithms (i.e., EPSDE, CSO, and SLPSO), ACODE can achieve the best performances in terms of response time, ground resolution, and fuel consumption, respectively.
System disturbances, such as the change of required service durations, the failure of resources, and temporary tasks during the scheduling process of data relay satellite network (DRSN), are ...difficult to be predicted, which may lead to unsuccessful scheduling of tasks. A high-efficiency and robust DRSN calls for smarter and more flexible disturbances elimination strategies. Here, we unify the above three system disturbances as temporary task arrival and extend the static scheduling model of DRSN. Specifically, we derive and define a scheduling model that unifies the static scheduling and dynamic scheduling processes. Meanwhile, we propose a
k
-step dynamic scheduling algorithm considering breakpoint transmission (
k
-steps-BT) to solve the above model. Based on the principle of backtracking algorithm and search tree,
k
-steps-BT can eliminate disturbances quickly by rescheduling tasks and can determine the rescheduling scheme when temporary tasks arrive. Finally, extensive experiments are carried out to verify the proposed model and algorithm. The results show that the proposed model and algorithm can significantly improve the task completion rate of dynamic scheduling without drastic adjustments to the static scheduling scheme.
A practical structural health monitoring (SHM) system based on Lamb wave for high-speed train car-body structures is presented. The system can detect the occurrence and report the location and ...probabilistic size of structural damage in real-time. Based on the theory of Lamb wave, a piezoelectric transducer array network is implemented to generate and acquire the detection signal by mounting on the surface of the structure to be monitored. An algorithm, that can locate, quantify and image the damage based on the damage index that indicates the differential between baseline signal and current acquired signal, is proposed to develop the system. Furthermore, a system framework of three-layer architecture and a diagnosis strategy are designed to build the SHM system. Finally, the system is implemented on China's latest high-speed train (CRH380A) operated along the Chengdu-Chongqing High-Speed Railway after well tuning then demonstrates the feasibility and reliable in practical application.
Condition-based maintenance is a more advanced maintenance strategy, owing to reducing inspection and maintenance costs and improving the reliability of targeted assets. The maintenance strategies in ...the Chinese Railway industry are transforming from plan-based to condition-based, which has heightened the need for the completion of relevant research. This study proposes an experience-based health status evaluation method for train wheels, which considers factors in the wheel manufacturing process, operational process and maintenance process. The manufacturing factors are indicated by material properties and press-fitting influence factor, while operational and maintenance factors are indicated by wheel diameter and damages. Based on the health evaluation values, a decision-making function is constructed to divide wheels into four categories: fine, attention, pre-alarm and scrapped. Finally, the PHM + Wheel system is developed based on the proposed health evaluation method to provide support for maintenance strategy. The PHM+Wheel system has been practically applied to the Chinese Railway industry with adequate feedback received.
The increasing demands for space-based data transmission pose a great challenge to task scheduling of tracking and data relay satellites (TDRSs). In order to improve the working efficiency and task ...completion rate of the data relay satellite network (DRSN), for the first time, we propose a novel application mode for DRSN, in which data breakpoint transmission is considered. In the mode of data breakpoint transmission, a single task can be reasonably split into multiple subtasks and thus scheduled in multiple time windows. At first, the task scheduling model of DRSN considering breakpoint transmission is defined. In addition, a two-stage method is designed to generate a high-quality initial solution. Moreover, we propose an adaptive variable neighborhood descent combined with a tabu list (AVND-TL) to iteratively improve the initial solution. In AVND-TL, two task reallocation neighborhood structures are incorporated and adaptively selected during the solution search process, which effectively prevent the algorithm from falling into local optimum. Finally, extensive experiments are carried out to verify that the proposed breakpoint transmission mode and AVND-TL together can significantly improve the task completion rate and resource utilization rate.
The COVID-19 pandemic calls for contactless deliveries. To prevent the further spread of the disease and ensure the timely delivery of supplies, this paper investigates a collaborative truck-drone ...routing problem for contactless parcel delivery (CRP-T&D), which allows multiple trucks and multiple drones to deliver parcels cooperatively in epidemic areas. We formulate a mixed-integer programming model that minimizes the delivery time, with the consideration of the energy consumption model of drones. To solve CRP-T&D, we develop an improved variable neighborhood descent (IVND) that combines the Metropolis acceptance criterion of Simulated Annealing (SA) and the tabu list of Tabu Search (TS). Meanwhile, the integration of K-means clustering and Nearest neighbor strategy is applied to generate the initial solution. To evaluate the performance of IVND, experiments are conducted by comparing IVND with VND, SA, TS, variants of VND, and large neighborhood search (LNS) on instances with different scales. Several critical factors are tested to verify the robustness of IVND. Moreover, the experimental results on a practical instance further demonstrate the superior performance of IVND.
The collaboration of drones and trucks for last-mile delivery has attracted much attention. In this paper, we address a collaborative routing problem of the truck-drone system, in which a truck ...collaborates with multiple drones to perform parcel deliveries and each customer can be served earlier and later than the required time with a given tolerance. To meet the practical demands of logistics companies, we build a multi-objective optimization model that minimizes total distribution cost and maximizes overall customer satisfaction simultaneously. We propose a hybrid multi-objective genetic optimization approach incorporated with a Pareto local search algorithm to solve the problem. Particularly, we develop a greedy-based heuristic method to create initial solutions and introduce a problem-specific solution representation, genetic operations, as well as six heuristic neighborhood strategies for the hybrid algorithm. Besides, an adaptive strategy is adopted to further balance the convergence and the diversity of the hybrid algorithm. The performance of the proposed algorithm is evaluated by using a set of benchmark instances. The experimental results show that the proposed algorithm outperforms three competitors. Furthermore, we investigate the sensitivity of the proposed model and hybrid algorithm based on a real-world case in Changsha city, China.
Satellite observation scheduling plays a significant role in improving the efficiency of Earth observation systems. To solve the large-scale multisatellite observation scheduling problem, this ...article proposes an ensemble of metaheuristic and exact algorithms based on a divide-and-conquer framework (EHE-DCF), including a task allocation phase and a task scheduling phase. In the task allocation phase, each task is allocated to a proper orbit based on a metaheuristic incorporated with a probabilistic selection and a tabu mechanism derived from ant colony optimization and tabu search, respectively. In the task scheduling phase, we construct a task scheduling model for every single orbit and solve the model by using an exact method (i.e., branch and bound, B&B). The task allocation and task scheduling phases are performed iteratively to obtain a promising solution. To validate the performance of the EHE-DCF, we compare it with B&B, three divide-and-conquer-based metaheuristics, and a state-of-the-art metaheuristic. Experimental results show that the EHE-DCF can obtain higher scheduling profits and complete more tasks compared with existing algorithms. The EHE-DCF is especially efficient for large-scale satellite observation scheduling problems.
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