Freight movement from various cities of origin in China, through consolidation centers and frontier ports, to different destinations in Europe within the China Railway Express logistics network is studied in this paper. The problem is formulated as a multi-capacity and multi-mode facility location-allocation problem with stochastic demand and delivery time which is modeled as a distributionally robust optimization problem. The objective is to minimize the total cost, which includes facility construction, transportation, and time delay costs. Given the stochastic nature of destination demand and transportation time, historical data is utilized to construct the ambiguity set of these stochastic parameters. Then, the proposed distributionally robust optimization problem is transformed into a two-stage deterministic optimization problem using probability and duality theory. An enhanced Benders decomposition algorithm is developed to solve the transformed problem that integrates several valid inequalities, multi-cut subproblem reformulation, and Pareto-optimal cuts to improve the performance of the algorithm. The computational experiments demonstrate that this improved Benders decomposition algorithm significantly outperforms the widely-used Gurobi solver in terms of solving speed. Finally, consolidation centers with different capacity levels are established in Xi’an, Urumqi, Chongqing, Shenyang, and Hohhot, and the corresponding transportation routes are given.

In recent years, remote sensing images have attracted a lot of attention because of their special value. However, images acquired by satellite sensors are usually low-resolution (LR), so remote sensing images are much more difficult to infer high-frequency details from compared with ordinary digital images, which means they cannot meet the needs of certain downstream tasks. In this letter, we propose a multiscale enhancement network (MEN), which uses multiscale features of remote sensing images to enhance the network’s reconstruction capability. Specifically, the network extracts the coarse features of LR remote sensing images using convolutional layers. Then, these features are fed into the multiscale enhancement module (MEM) proposed by this network, which uses a combination of convolutional layers with multiple convolutional kernel sizes to refine the extraction of multiscale features, and finally, the final reconstructed image is generated by the reconstruction module. Extensive experiments show that MEN achieves significant reconstruction advantages in both objective and subjective aspects.

Ramp metering has been considered as one of the most effective approaches of dealing with the traffic congestion on the freeways. The modelling of the freeway traffic flow dynamics is challenging because of its non-linearity and uncertainty. Recently, Koopman operator, which transfers a non-linear system to a linear system in an infinite-dimensional space, has been studied for modelling complex dynamics. In this paper, we propose a data-driven modelling approach based on neural networks, denoted by deep Koopman model, to learn a finite-dimensional approximation of the Koopman operator. To consider the sequential relations of the ramps and main roads on the freeway, a long short-term memory network is applied. Furthermore, a model predictive controller with the trained deep Koopman model is proposed for the real-time control of the ramp metering on the freeway. To validate the performance of the proposed approach, experiments based on the simulation in the traffic simulation software Simulation of Urban MObility (SUMO) environment are conducted. The results demonstrate the effectiveness of the proposed approach on both the dynamics prediction and the real-time control of the ramp metering.

Due to the breakthrough of wireless power transfer technology, wireless rechargeable sensor networks (WRSNs) have the potential to provide sustainable work. Most existing researches on WRSNs usually focus on the cases that mobile charging vehicle moves freely through the sensors. However, for some applications, such as bridge monitoring, WRSNs are implemented in a three-dimensional space with obstacles, so the charging path may be blocked by the obstacles. To cope with this problem, charging scheduling to replenish a wireless rechargeable sensor network for bridge monitoring by an unmanned aerial vehicle (UAV) is studied. The problem is formulated as an optimization problem through optimizing UAV navigation path and sensor energy allocation collaboratively. This optimization problem is hard to be solved as both path navigation and energy allocation are required to be optimized simultaneously. To circumvent this challenge, an improved ant colony system algorithm (IM-ACS) is proposed to plan the trajectory of the UAV between sensors. By integrating enhancement factors and dynamic pheromone intensity coefficients, the convergence of the algorithm is accelerated. Then, a two-stage algorithm is proposed to schedule charging sequence and assign energy with limited energy carried by the UAV in each charging period. Experiments and simulations show that the proposed approach achieves shorter feasible trajectory paths and longer network lifetime than those obtained by the compared methods.

Ramp metering offers great potential to mitigate traffic congestion and improve freeway management efficiency under traffic congestion conditions. This paper proposes an optimization program for freeway dynamic ramp metering based on Cell Transmission Model (CTM). This problem has been formulated as a discrete time optimal control problem with smooth state equations and constraints to meter traffic inflow from on-ramps. In the proposed model, the ‘min’ operators in the primal CTM are non-differentiable and thus, the corresponding optimal control problem cannot be solved directly using conventional gradient based methods. In this paper, we introduce a smooth approximation to approximate the ‘min’ operators and then a unified computational approach is developed to solve the problem. Theoretical analysis is carried out, showing that the optimal solution obtained from the approximated problem converges to the optimal solution of the primal CTM. Compared to the classical inequality relaxation method, our method can resolve the flow holding-back problem and reduce under fundamental diagram phenomenon. Compared with the Big-M method, our method has better efficiency. To achieve the desired traffic response control in real application, a series of online optimal control problems are solved using Model Predictive Control (MPC). Simulation studies show that our method can significantly improve freeway traffic management efficiency.

Perimeter control is to manipulate traffic flows in different regions through adjusting traffic signals at the border of the regions. Traditionally, the complete trips in a region are assumed to be dependent on the current accumulated vehicles in this region. This assumption is invalid because the vehicle needs time to complete its trip in a region. In order to solve this shortcoming, some recent studies have introduced a time-delay dynamical system to describe the dynamical behaviour of the accumulated vehicles in a region. However, in these studies, the perimeter control problem is formulated as a tracking problem with a given optimal reference point. In reality, such an optimal reference point is unavailable in advance. This paper will fill this gap through formulating perimeter control as an optimal control problem governed by a state-dependent time delay system. The control parametrization technique and an exact penalty method are introduced to solve such a challenging optimal control problem. Model predictive control is applied to obtain close-loop solution through solving a series of online optimal control problems. Some experiments are performed to demonstrate the effectiveness of our method.