Optimized Base Station Sleeping and Smart Grid Energy Procurement Scheme to Improve Energy Efficiency

In this chapter, the authors propose a joint BS sleeping strategy, resource allocation, and energy procurement scheme to maximize the profit of the network operators and minimize the carbon emission. Then, a joint optimization problem is formulated, which is a mixed-integer programming problem. To solve it, they adopt the bi-velocity discrete particle swarm optimization (BVDPSO) algorithm to optimize the BS sleeping strategy. When the BS sleeping strategy is fixed, the authors propose an optimal algorithm based on Lagrange dual domain method to optimize the power allocation, subcarrier assignment, and energy procurement. Numerical results illustrate the effectiveness of the proposed scheme and algorithm.

Joint Channel Allocation and Power Control for Uplink NOMA-Assisted Multi-UAV Networks

The explosive growth of data leads to that the traditional wireless networks cannot enable various quality of service (QoS) communication for cellular-connected multi-UAV (unmanned aerial vehicle) networks. To overcome this obstacle, we solve the joint optimization problem of channel allocation and power control for uplink NOMA-assisted multi-UAV networks. Firstly, we design a mixed integer nonlinear programming framework, where the channel gains are characterized with integral form in time interval and sorted in nondescending order as the priority index of the decoded signal. In order to propose a feasible algorithm, the initial power levels of UAVs are obtained and integrated into the original problem which is reduced to integer programming problem. Then, the UAVs whose channel gain differences satisfy the constraints will be divided into a group to share the same channel, while the initial power levels of UAVs are adjusted to get a more satisfactory initial solution for power control. Combining the solution of channel allocation and the initial power levels, we solve power control problem with asynchronous update mechanism until the power levels of UAVs remain unchanged. Finally, we propose a channel allocation algorithm and a power control algorithm with the asynchronous optimization mechanism, respectively. Simulation results show that the proposed algorithms can effectively improve the network performance in terms of the aggregated rate.

Collaborative Vehicle Routing Problem in the Urban Ring Logistics Network under the COVID-19 Epidemic

This paper explored the problem of collaborative vehicle routing in the urban ring logistics network (Co-VRP-URLN) during the COVID-19 epidemic. According to the characteristics of urban distribution and the restriction of traffic all over China during this period, this study mainly considers a common distribution mode of order exchange through the outer ring of the city and then solves the vehicle routing problem of distribution, which belongs to a special multidepot vehicle routing problem with time windows. According to the definition of the problem, the corresponding mixed-integer programming problem of multicommodity flow is established, and the variable neighborhood search algorithm is designed in detail to solve it. The effectiveness of the algorithm is verified by a standard example, and the benefits of joint distribution are revealed through the improved standard example. At last, the influence of different distribution centers is compared. The results show that this model can significantly improve the distribution efficiency within the city under the restriction of traffic.

Event-Triggered Intervention Framework for UAV-UGV Coordination Systems

Air-ground coordination systems are usually composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV). In such a system, UAVs can utilize their much more perceptive information to plan the path for UGVs. However, the correctness and accuracy of the planned route are often not guaranteed, and the communication and computation burdens increase with more sophisticated algorithms. This paper proposes a new type of air-ground coordination framework to enable UAVs intervention into UGVs tasks. An event-triggered mechanism in the null space behavior control (NSBC) framework is proposed to decide if an intervention is necessary and the timing of the intervention. Then, the problem of whether to accept the intervention is formulated as an integer programming problem and is solved using model predictive control (MPC). Simulation results show that the UAV can intervene in UGVs accurately and on time, and the UGVs can effectively decide whether to accept the intervention to get rid of troubles, thereby improving the intelligence of the air-ground coordination system.

Energy-Efficient Allocation for Multiple Tasks in Mobile Edge Computing

Mobile edge computing (MEC) allows a mobile device to offload tasks to the nearby server for remote execution to enhance the performance of user equipment. A major challenge of MEC is to design an efficient algorithm for task allocation. In contrast to previous work on MEC, which mainly focuses on single-task allocation for a mobile device with only one task to be completed, this paper considers a mobile device with multiple tasks or an application with multiple tasks. This assumption does not hold in real settings because a mobile device may have multiple tasks waiting to execute. We address the problem of task allocation with minimum total energy consumption considering multi-task settings in MEC, in which a mobile device has one or more tasks. We consider the binary computation offloading mode and formulate multi-task allocation as an integer programming problem that is strongly $NP$-hard. We propose an approximation algorithm and show it is a polynomial-time approximation scheme that saves the maximum energy. Therefore, our proposed algorithm achieves a tradeoff between optimality loss and time complexity. We analyze the performance of the proposed algorithm by performing extensive experiments. The results of the experiments demonstrate that our proposed approximation algorithm is capable of finding near-optimal solutions, and achieves a good balance of speed and quality.

Mathematical experiment as a tool for modeling the freight transportation process

The process of multimodal freight transportation on a railway loop is investigated, for which a multicriteria optimization model with time indicators is created, which is a nonlinear integer programming problem. The developed approach is based on egalitarian and utilitarian principles in the welfare theory and allows (along with Pareto optimal transportation plans) to find other plans that can be considered rational from the point of view of maintaining the balance of interests of the transportation process participants. A mathematical experiment is an effective heuristic tool in research. The algorithm for solving the optimization problem is implemented in the environment of a computer algebra system and brought to numerical results.

Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids

Wireless sensor networks are used to monitor the operating status of the microgrids, which can effectively improve the stability of power supplies. The topology control is a critical issue of wireless sensor networks, which affects monitoring data transmission reliability and lifetime of wireless sensor networks. Meanwhile, the data acquisition accuracy of wireless sensor networks has a great impact on the quality of monitoring. Therefore, this paper focuses on improving wireless sensor networks data acquisition satisfaction and energy efficiency. A joint acquisition time design and sensor association optimization algorithm is proposed to prolong the lifetime of wireless sensor networks and enhance the stability of monitoring, which considers the cluster heads selection, data collection satisfaction and sensor association. First, a multi-constrained mixed-integer programming problem, which combines acquisition time design and sensor association, is formulated to maximize data acquisition satisfaction and minimize energy consumption. To solve this problem, we propose an iterative algorithm based on block coordinate descent technology. In each iteration, the acquisition time is obtained by Lagrangian duality. After that, the sensor association is modeled as a 0–1 knapsack problem, and the three different methods are proposed to solve it. Finally, the simulations are provided to demonstrate the efficiency of the algorithm proposed in this paper.

Recourse in Kidney Exchange Programs

We introduce the problem of selecting patient-donor pairs in a kidney exchange program to undergo a crossmatch test, and we model this selection problem as a two-stage stochastic integer programming problem. The optimal solutions of this new formulation yield a larger expected number of realized transplants than previous approaches based on internal recourse or subset recourse. We settle the computational complexity of the selection problem by showing that it remains NP-hard even for maximum cycle length equal to two. Furthermore, we investigate to what extent different algorithmic approaches, including one based on Benders decomposition, are able to solve instances of the model. We empirically investigate the computational efficiency of this approach by solving randomly generated instances and study the corresponding running times as a function of maximum cycle length, and of the presence of nondirected donors. Summary of Contribution: This paper deals with an important and very complex issue linked to the optimization of transplant matchings in kidney exchange programs, namely, the inherent uncertainty in the assessment of compatibility between donors and recipients of transplants. Although this issue has previously received some attention in the optimization literature, most attempts to date have focused on applying recourse to solutions selected within restricted spaces. The present paper explicitly formulates the maximization of the expected number of transplants as a two-stage stochastic integer programming problem. The formulation turns out to be computationally difficulty, both from a theoretical and from a numerical perspective. Different algorithmic approaches are proposed and tested experimentally for its solution. The quality of the kidney exchanges produced by these algorithms compares favorably with that of earlier models.

Two-Step Intelligent Control for a Green Flexible EV Energy Supply Station Oriented to Dual Carbon Targets

As China proposes to achieve carbon peak by 2030 and carbon neutrality by 2060, as well as the huge pressure on the power grid caused by the load demand of the energy supply stations of electric vehicles (EVs), there is an urgent need to carry out comprehensive energy management and coordinated control for EVs’ energy supply stations. Therefore, this paper proposed a two-step intelligent control method known as ISOM-SAIA to solve the problem of the 24 h control and regulation of green/flexible EV energy supply stations, including four subsystems such as a photovoltaic subsystem, an energy storage subsystem, an EV charging subsystem and an EV battery changing subsystem. The proposed control method has two main innovations and contributions. One is that it reduces the computational burden by dividing the multi-dimensional mixed-integer programming problem of simultaneously optimizing the 24 h operation modes and outputs of four subsystems into two sequential tasks: the classification of data-driven operation modes and the rolling optimization of operational outputs. The other is that proper carbon transaction costs and carbon emission constraints are considered to help save costs and reduce carbon emissions. The simulation analysis conducted in this paper indicates that the proposed two-step intelligent control method can help green/flexible EV energy supply stations to optimally allocate energy flows between four subsystems, effectively respond to peak shaving and valley filling of power grid, save energy costs and reduce carbon emissions.

A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions

Protein–RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequences and structures involved in PRIs is important for unraveling such processes. Because of the expensive and time-consuming techniques required for experimental determination of complex protein–RNA structures, various computational methods have been developed to predict PRIs. However, most of these methods focus on predicting only RNA-binding regions in proteins or only protein-binding motifs in RNA. Methods for predicting entire residue–base contacts in PRIs have not yet achieved sufficient accuracy. Furthermore, some of these methods require the identification of 3D structures or homologous sequences, which are not available for all protein and RNA sequences. Here, we propose a prediction method for predicting residue–base contacts between proteins and RNAs using only sequence information and structural information predicted from sequences. The method can be applied to any protein–RNA pair, even when rich information such as its 3D structure, is not available. In this method, residue–base contact prediction is formalized as an integer programming problem. We predict a residue–base contact map that maximizes a scoring function based on sequence-based features such as k-mers of sequences and the predicted secondary structure. The scoring function is trained using a max-margin framework from known PRIs with 3D structures. To verify our method, we conducted several computational experiments. The results suggest that our method, which is based on only sequence information, is comparable with RNA-binding residue prediction methods based on known binding data.