应急电力物料运输中的无人机调度

应急电力物料运输中的无人机调度: Comparison

Please note this is a comparison between Version 2 by Wendy Huang and Version 1 by junjie wang.

稳定的物料运输对于灾后快速恢复电力系统至关重要。基于无人机的物料运输可以绕过地面运输的限制并减少运输时间。然而，目前的无人机飞行轨迹分布优化模型无法满足灾后山区应急救灾物资配送的需要。制定合理的应急配电计划可以显著加快配电系统的恢复速度。Stable material transportation is essential for quickly restoring the power system following a disaster. Drone-based material transportation can bypass ground transportation’s limitations and reduce transit times. However, the current drone flight trajectory distribution optimization model cannot meet the need for mountainous emergency relief material distribution following a disaster. Creating rational emergency distribution plans can significantly speed up the distribution system’s recovery.

material transportation
emergency distribution
drone
drone scheduling
VRPD
fight trajectory
algorithm
task assignment

1. 简介Introduction

制定合理的应急配电计划可以显著加快配电系统的恢复速度Creating rational emergency distribution plans can significantly speed up the distribution system’s recovery [1,2]。传统的应急配送严重依赖运输网络，由于中国西部山区自然灾害频繁和地形条件复杂，交通网络容易受到重大破坏. Traditional emergency distribution relies heavily on the transportation network, which is susceptible to significant damage due to the mountainous regions of western China’s frequent natural catastrophes and complicated topographical conditions [3]。无人机空投材料技术可以成功解决这些问题。. Drone airdrop material techniques can successfully address these issues.

如何进行快速无人机飞行轨迹分配是解决问题的关键之一。无人机的车辆路径问题（How to perform fast drone flight trajectory assignment is one of the keys to solving the problem. The Vehicle Routing Problem of Drones (VRPD）是车辆路径问题（VRP）的一种变体，已被研究人员研究。在经典的VRPD问题中，不同的需求和实际情况可以决定目标函数，如物料的最短运输交货时间), a variation of the Vehicle Path Problem (VRP), has been researched by researchers. In the classic VRPD problem, Different needs and the actual situation can determine the objective function, such as the shortest transport delivery time of the material [4,5,6]和时间窗口约束 and the time window constraints [6]。然而，目前许多研究的针对性很差，只抽象到传统的材料输送问题，忽视了各种运输问题和问题动态之间的区别，如果在案文中考虑到目标点的特点，这将更加相关。. However, many of the current studies are poorly targeted, abstracted only to conventional material transport problems, ignoring the distinctions between various transport problems and the dynamics of the problem, which would be more pertinent if the target point’s characteristics were taken into account in the text.

找到Finding the best solution to the VRPD问题的最佳解决方案是一项重大挑战。启发式算法在紧急情况下更有效地解决VRPD问题 problem is a significant challenge. Heuristic algorithms are more efficient at solving the VRPD problem in emergency settings [7]，例如粒子群算法（, such as the Particle Swarm Algorithm (PSO）) [8]，遗传算法（, the Genetic Algorithm (GA）) [9]和动态人工蜂群（, and the Dynamical Artificial Bee Colony (DABC）) [10]算法。在这些算法中， algorithm. Among these algorithms, the PSO具有计算简单、收敛快等优点，适合应用于快速需求的应急救援工作分配 has the benefits of simple computing and quick convergence and is appropriate for application in rapid-demand emergency rescue work allocation [11]。随着迭代次数的增加，种群多样性的快速下降使得. The fast decline in population variety as the number of iterations variegation rises makes PSO极易受到局部最优的影响，并且高度依赖于参数设置。 extremely vulnerable to local optima and highly reliant on parameter settings.

2.电力应急配网与无人机配网、无人机作战轨迹问题 Electricity Emergency Distribution and Drone Distribution, Drone Fight Trajectory Issues

在突然停电的情况下，必须及时提供应急物资，以尽量减少停电造成的生命和财产损失，恢复生活和工作环境。A timely provision of emergency supplies is necessary in the case of a sudden power outage to minimize the loss of lives and property caused by the outage and to restore the living and working environment. Hou等人 et al. [2]提出了一种基于节点积分权重的资源满足的电气应急救援运输模型，创建了一个材料供应量和时间精简量最小的目标函数。 proposed an electrical emergency rescue transport model based on node-integrated weights for resource satisfaction, creating an objective function with the least amount of material supply and time leanness. A multi-point fault repair optimization model was created by Gao等 et al. [14]考虑了急救材料的分配、维修团队的配合以及损坏设备的修复顺序，创建了多点故障修复优化模型。, considering the distribution of first aid materials, the cooperation of repair teams, and the order of fixing broken equipment. 然而，传统的电气材料救援主要存在于城市地区，使其在有利的交通和道路条件下更加实用。传统方法的弊端在路况较差的地区更为严重，使得无人机运输成为提高运输效率的更有效手段。以下是与无人机运输相关的文章。However, traditional electrical material rescue is primarily found in urban locations, making it more practical in favorable traffic and road conditions. The drawbacks of the conventional method are more severe in areas with poor road conditions, making drone transportation a more effective means to increase transportation effectiveness. Below are articles related to drone transport. 通过将无人机的功耗建模为多行程无人机路由问题模型中有效载荷和跳闸时间的非线性函数，考虑时间窗口，By modeling the power consumption of a drone as a nonlinear function of payload and trip time in a multi-traveling Drone routing problem model, considering a time window, Cheng等人 et al. [15]进一步扩展了 further extended Dorling的研究。他们的模型提供逻辑和子梯度切割来处理非线性幂函数，使用分支和切割方法解决无人机布线问题。在’s study. Their model offers logical and sub-gradient cuts to handle nonlinear power functions, using the branch-and-cut method to resolve the drone routing issue. In Gentili等人 et al.’s [6]的优化问题中，紧急医疗用品的价值根据其易腐烂性最小化。考虑到无人机在运输医疗用品时的电池寿命，他们假设每个平台一次只能有一架无人机为一个节点提供服务。 optimization issue, the value of emergency medical supplies is minimized depending on their perishability. Considering the drone’s battery life while transporting medical supplies, they hypothesize that each platform would only have one drone that could service one node at a time. The application of VRPD在异构无人机中的应用也在几项研究中进行了研究。VRPD在异构无人机中的应用也在几项研究中进行了研究。对于异构固定舰队无人机路由问题， for heterogeneous drones is also investigated in several of the studies. The application of VRPD for heterogeneous drones is also investigated in several of the studies. For a heterogeneous fixed fleet drone routing problem, Chowdhury等人 et al. [16]提出了一个混合整数线性规划模型来解决灾后检查的成本，因为有几个方面。 suggested a mixed integer linear programming model to resolve the cost of post-disaster inspections because of several aspects. Chen等人 et al. [17]处理了多区域系统中不同能力的无人机的无人机战斗轨迹规划问题。他们最初创建了一种将区域分组为聚类的算法，从基于密度的聚类技术中汲取灵感。然后，他们获得了无人机执行覆盖任务的理想点对点路径的近似值。在 dealt with the drone fight trajectory planning problem for drones with different capabilities in a multi-area system. They initially created an algorithm to group regions into clusters, drawing inspiration from density-based clustering techniques. They then obtained approximations of the ideal point-to-point pathways for drones to carry out coverage tasks. In a different work by Chen等人 et al. [18]的另一份工作中，他们专注于异构无人机的覆盖作战轨迹规划问题。他们提出了一种基于蚁群系统（, they concentrated on the issue of coverage fight trajectory planning for heterogeneous drones. They suggested a method based on the Ant Colony System (ACS）的方法，以获得足够的无人机路径，并彻底有效地覆盖每个区域。文献显示，无人机配送研究最大的障碍是约束，这也是传统车辆路线问题模型不适用于无人机物流的原因之一。) to obtain enough drone pathways and cover every region thoroughly and effectively. According to the literature, the most significant barrier to drone distribution research is the constraint, which is one of the reasons why conventional vehicle route problem models are inapplicable to drone logistics. 以往的大多数研究都从无人机特有的角度考察了这个问题，忽略了供应及时性和无人机调度之间的协调关系，以及目标需求和无人机的安全考虑。构建专注于交付电气材料的无人机调度模型至关重要，因为用于紧急运输动力无人机的模型仍然不常见。Most previous studies have examined the issue from a drone-specific angle, ignoring the coordinated relationship between supply timeliness and drone scheduling, as well as target demand and the drones’ safety considerations. The construction of a drone scheduling model focusing on delivering electrical materials is crucial because models for the emergency transport of power drones are still uncommon.

3. Algorithm 解决无人机任务分配的算法for Solving UAV Task Assignment

在无人机任务分配中，启发式算法更常用In drone task assignments, heuristic algorithms are more frequently utilized [19]。基于此，. Based on this, Wu等 et al. [20]对遗传算法（ perform the secondary selection operation of the genetic algorithm (GA）进行二次选择运算以提高种群多样性，二次选择运算采用改进的模拟退火算法（SA）更有效地解决协同多任务分配问题。Han等人) to improve the population diversity, and the secondary selection operation adopts the improved simulated annealing algorithm (SA) to solve the collaborative multitasking allocation problem more effectively. Han et al. [21]提出了一种模糊精英策略遗传算法来处理复杂的问题。为了提高其逃脱局部最优陷阱并加速收敛的能力， suggested a fuzzy elite strategy genetic algorithm to handle complicated issues. To increase its capacity to escape local optimal traps and hasten convergence, Wang等人 et al. [22]建议将模拟退火（ suggested combining Simulated Annealing (SA）和大邻域搜索（LNS）算法结合起来。Liu等人) and Large Neighborhood Search (LNS) algorithms. Liu et al. [23]还提出了一种结合 also proposed a collaborative optimization method combining GA和聚类方法的协同优化方法，以满足无人机对森林火灾的任务分配。Li等人 and clustering methods to satisfy the task assignment of forest fires by drones. An upgraded cellular automaton (CA) and an optimal spanning tree technique were utilized by Li et al. [24]利用升级的元胞自动机（CA）和最优生成树技术来构建路径网络并找到各个端点之间的最佳路由。 to build the path network and find the best routes between various endpoints. Zhang等人 et al. [25]根据粒子质量动态划分粒子群，并改变了算法的拓扑结构。此外，任务处理问题中的动态问题也很常见。此外， dynamically divided the particle swarm based on the particle mass and changed the topology of the algorithm. In addition, dynamic problems in tasking problems are common. Additionally, the PSO算法已被用于其他组合作为启发式方法：在他们的研究中，Geng等人 algorithm has been employed in additional combinations as a heuristic method: in their studies, Geng et al. [26]提出了一种量化的粒子群优化算法，用于无人机集群的任务分配问题 proposed a quantified particle swarm optimization algorithm for the task allocation problem of UAV clusters;Shao等人. Shao et al. [27]在构建量化粒子群优化技术的情况下，提出了一种基于离散粒子群优化的混合策略，用于多对一任务规划问题。 proposed a hybrid strategy based on discrete particle swarm optimization for the many-to-one task planning problem in the case of a constructed a quantized particle swarm optimization technique. The simulated annealing algorithm is enhanced by Chen等人 et al. [28]使用 using a Levi分布策略增强了模拟退火算法，它适用于动态和静态任务分配模型。Yang等 distribution strategy, and it works well for both dynamic and static task assignment models. Yang et al. [29]采用基于传感器网络和移动信息的无人机集群分布式理性聚类算法，提高无人机任务分配完成率，为任务分配提供了一种新的分布式算法。 use a distributed rational clustering algorithm for UAV clusters based on sensor networks and mobile information to improve the completion rate of UAV task assignments, providing a new distributed algorithm for task assignments.