Container collection should be completed within the specified time limit to prevent the arriving ships from being unable to leave the port on schedule. The storage yard should formulate a container allocation plan and yard crane scheduling plan based on the scheduled arrival time of container delivery and loading. Otherwise, the plan will become invalid, which will lead to the problem of container turnover and loading inefficiency. Aimed at the impact of the non-engagement of container delivery and truck collection on the container location allocation plan, yard crane scheduling plan, and the waiting time of the truck, an optimization problem of container location allocation and yard crane scheduling in multi-container areas under non-engagement of container delivery and truck collection is proposed. Considering the constraints such as the booking period of the truck and the safe distance between the two yard cranes, a mixed integer programming model is established with the goal of minimizing the time of port concentration. The hybrid genetic variable neighborhood algorithm is used to solve the model. A comparison of the experimental results of different algorithms indicates that the algorithm in this paper has a fast convergence speed and excellent solution results. Based on the actual arrival time of the container truck, a disturbance recovery strategy is proposed to measure the impact of non-engagement on the completion time and container truck waiting time in different scenarios, an interference recovery strategy is proposed. Experiments show that the interference recovery strategy proposed in this paper not only shortens the job completion time and the number of containers overturned due to box delivery breach, but also reduces the waiting time of container delivery trucks.
In order to realize the quantitative estimation of tire vertical force, the algorithm of heavy-loaded tire vertical force estimation based on circumferential strain analysis is studied. A finite element analysis model of 16.00R20 tire is established. A comparison of tire loading test and modal vibration test indicates that the vertical stiffness error and vibration frequency error of the model are less than 7.79% and 5.49% respectively, which verifies the validity of the model. Using the finite element method, the influence of vertical force on tire grounding characteristics and circumferential strain of inner liner is studied. The characterization index of tire contact angle is proposed and verified by circumferential strain analysis, and the errors of the three indexes are all less than 8%. Taking the grounding angle and grounding length as identification features, the vertical force estimation model is established by combining grey wolf optimization (GWO) and the support vector regression (SVR), and the estimation accuracy is verified by finite element simulation. The results indicate that the characterization index in combination with the of characteristic point spacing angle of zero-order, first-order and second-order derivative of strain curve can accurately estimate tire contact angle. The error between the estimated value of the vertical force estimation algorithm based on GWO-SVR and the finite element simulation value is less than 1.8%.
The evalution of link resilience importance is essential for improving the seismic resilience level of the road network. Using resilience achievement worth (RAW) as evaluation index, an evaluation method of link importance based on seismic resilience of road networks was proposed. With the help of the bidirectional inference ability of dynamic Bayesian network (DBN), taking the initial DBN as the benchmark and the link connectivity at different times as the evidence, the resilience curve of the road network was updated, the RAW was calculated, and the link resilience importance at different times was evaluated. Taking the local road network in Shinan District of Qingdao as an example, the evaluation method of link importance was verified. The results show that the seismic resilience importance varies with each link at the same time. The resilience importance of the same link is positively correlated with maintenance rate. The resilience importance of different links varies in sensitivity to time. The proposed importance evaluation method redefines and quantifies the seismic resilience importance of links at different times, and identifies the links with high resilience importance and sensitivity to time. The post-earthquake recovery strategy that inclines the limited maintenance resources to these links and the pre-earthquake prevention strategy that reinforces the links with higher resilience importance can more efficiently improve the seismic resilience of the road network.
Aimed at the problems of insufficient search range and optimization performance in 3D path planning of unmanned aerial vehicles (UAVs), and the lack of optimization accuracy of the existing slime mould algorithm (SMA), which is easy to fall into local optimization, a 3D path planning method for UAV based on adaptive slime mould algorithm optimization is proposed. First, according to the actual environment that the UAV passes through, the 3D terrain, the threat source and the constraints of the AUV were established. Next, for the problem of insufficient search range, an improved Logistic chaotic map is designed to increase the diversity of the population and expand the search range, which improves the global search ability of SMA. Then, a nonlinear adaptive inertia weight factor is designed to change the linear convergence method into nonlinear convergence, and the weight value is used to update the position of the slime mould, which improves the convergence speed. Finally, in the later stage of the algorithm, the adaptive cauchy mutation is designed, which increases the search space of the slime mould and improves the optimization accuracy. The experimental results show that GSMA has a shorter and smoother path, a faster convergence, a higher optimization accuracy, and a lower energy consumption compared with the gray wolf optimizer (GWO) algorithm, the SMA, and the seagull algorithm (SOA), which further improves the path planning capability of the UAV.
Aimed at the lack of awarness of safety and airworthiness state perception ability of airport ice runway and the new demand of interaction of runway surface condition report, a multi-scale feature fusion based ice and snow state perception model of airport runway is proposed. Based on the YOLOX-s model, first, the global context block (GC block) is introduced into the backbone feature extraction network to obtain more abundant shallow and deep features. Then, the PANet networks in neck are replaced with the bi-directional feature pyramid network (BiFPN) to improve the feature fusion ability. Afterwards, an adaptive spatial feature fusion (ASFF) structure is added to the tail of the enhanced feature extraction network to further enhance the feature fusion effect. Finally, α-EIoU is used to optimize the loss function to improve the convergence speed and accuracy of the model. The experimental results show that the improved YOLOX-s model has an average accuracy of 91.53% in the snow and ice pollutant data set obtained from the runway snow and ice experimental system, which is 4.68% higher than the original YOLOX-s model, and can provide decision-making support for airport runway snow removal operations.
