Flexible scheduling is one of the most significant core techniques for intelligent manufacturing systems. Realization of an optimized schedule through flexible resources assignment is critical to the application and popularization of flexible scheduling, especially in uncertain manufacturing environments. In this paper, we consider flexible job shop scheduling with uncertain processing time represented by fuzzy numbers, which is named fuzzy flexible job shop scheduling. We propose an effective hybrid cooperative co-evolution algorithm (hCEA) for the minimization of fuzzy makespan. The hCEA combines particle swarm optimization with the genetic algorithm to improve the convergence ability. A parameter self-adaptive strategy is applied to the problems with different scale effectively as well. Five benchmarks and three large-scale problems with fuzzy processing time are adopted to test the hCEA. Computational results show that the hCEA performs better than the existing methods from the literature.

The sudden outbreak of COVID-19 brings many unpredictable situations to human travel, such as temporarily closed highways, parking lots, etc. The scenarios mentioned above will lead to a large backlog of vehicles, and the requirements of Internet of vehicle (IoV) applications increase sharply in a period of short time correspondingly. Mobile edge computing (MEC) is a key enabling technology that can guarantee the diverse requirements of IoV applications through the optimization of resource scheduling. However, the sharp increasing in requirements of IoV applications caused by the congestion of highways or parking lots still bring great challenges to the deployment of traditional MEC. Therefore, in this paper, we construct an unmanned aerial vehicle (UAV) enabled MEC system, in which the data generated from IoV applications is processed by offloading to UAVs with MEC servers to ensure the efficiency of data processing and the response time of IoV applications. In order to approximate real-world UAV enabled MEC system, we consider the stochastic offloading and downloading processing time. Moreover, the priority constraints of sensors from the same vehicle are taken into consideration since they have different importance degrees. Then, we propose an Markov network-based cooperative evolutionary algorithm (MNCEA) to search out the optimal UAV scheduling solution to guarantee the shortest response time, in which the solution space is divided into multiple sub-solution spaces with the help of MN structure and parameters. Finally, we construct multiple simulation experiments with different probability distributions to simulate uncertainty factors. The simulation results verify the validity of MNCEA compared with the state-of-the-art methods, which is reflected by the shortest response time of requirements of IoV applications

Accidents are inevitable in the transportation systems; however, harnessing the big data generated from traffic accidents can significantly enhance the intelligence of the transportation system. Multiple unmanned aerial vehicles (multi-UAVs), owing to its flexibility and collaboration, can rapidly track the accidents and gather the acquire relevant data with reasonable deployment. Therefore, we propose an architecture utilizing multiple UAVs to track traffic accidents (targets) and collect relevant data. However, dynamic factors such as no-fly zones and reappearing targets in real-world traffic environments pose challenges to the rapid deployment of UAVs with current algorithms. In this paper, we design a model for deploying multiple UAVs, considering no-fly zones and dynamically reappearing targets. This model aims to optimize UAV deployment by minimizing both the flight distance of each UAV and the associated risk. Risk is defined as the urgency of processing accident scenes, which escalates with the elapsed time from the initial appearance of the targets to their processing. To address the joint UAV deployment problem, we first introduce an algorithm termed preprocessing and group-crossover nondominated sort genetic algorithm II (PGC-NSGAII). In PGC-NSGAII, we employ a group-based selection crossover (GBSC) method to enhance the algorithm’s search capability. This method segregates the initial population into two groups, selecting two individuals for crossover within each group. Secondly, building upon the framework of NSGAII, we incorporate a novel preprocessing component to enrich solution diversity. Thirdly, we develop a prediction method for dynamic environment parameters and a no-fly zone avoidance strategy for multi-UAV deployment. Finally, our experimental results demonstrate that PGC-NSGAII surpasses other existing methods in scenarios with varying numbers of UAVs or targets. Compared with state-of-the-art optimization methods, PGC-NSGAII proves to be more efficient in UAV deployment in dynamic environment.