针对山区震后道路损毁,电力和通信中断等复杂极端应急救援场景,直升机和无人机是应急救援的关键装备。传统救援方式通常将二者作为独立装备分别调度。为此,该文突破“阶段化”的优化局限,搭建了异构航空器“并联”救援范式和基于性能差异的混合任务分配模型,在全局层面优化了异构平台的协同效率。首先,综合考虑救援收益和时效性,以救援满意度为核心优化目标,并结合受灾点动态优先级和航空器性能约束等关键因素,搭建了异构航空器联合任务分配模型;其次,针对传统非支配排序遗传算法-II (non-dominated sorting genetic algorithm II,NSGA-II),提出了一种改进的变异操作策略;最后,以2008年汶川地震为背景,在不同规模救援场景下验证了所提模型和算法的有效性。结果表明：直升机-无人机协同救援模式能够优先响应重伤人员救援需求,有效应对复杂灾情;融合贪心策略的改进NSGA-II算法在求解质量方面优于传统算法,所得任务分配方案在资源利用率和救援时效性方面效果较好。该文研究结果可为制定山区应急救灾的航空器协同救援策略提供理论参考。
Abstract
[Objective] After an earthquake, helicopters and unmanned aerial vehicles (UAVs) can be effective means of emergency rescue response when roads are destroyed, power outages occur, and communications are disrupted in hilly and mountainous areas. Owing to the different functionalities and uses of UAVs and helicopters, which operate in different airspaces, existing research tends to schedule and optimize these two types of rescue equipment separately. If they do not account for the cooperation between the two heterogeneous aircraft, the overall rescue efficiency is reduced. [Methods] This study aims to optimize helicopter and UAV teams for search and rescue operations in a post-earthquake environment. This study proposes an innovative parallel rescue framework for the simultaneous coordination of heterogeneous aircraft, rather than classical sequential methods. This study focuses on a performance-based hybrid task allocation model that systematically leverages the specificities of different aircraft types while simultaneously considering the satisfaction of the rescue as a key optimization. This optimization goal balances operational benefits with timely mission accomplishment. It is measured by the total distance flown and the overall satisfaction with task completion. The mathematical model dynamically adapts the priority of affected areas. It also includes several operational constraints, such as the number of aircraft, the frequency of service, payload capacity, endurance limits, and time windows for effective rescue response. To overcome this complex, multiobjective optimization issue, this study designed an improved evolutionary algorithm called greedy-enhanced non-dominated sorting genetic algorithm Ⅱ(GE-NSGA-Ⅱ), which was developed based on an improved mutation strategy adapted to population distribution characteristics. [Results] The algorithm created a mechanism for adjusting the greedy-adaptive intensity and ensured robust randomness during the initial search phase. Over time, as the process evolved, the local search intensity improved. Even with adjustments, the method remained stronger and more robust than others due to improved global search capabilities. Moreover, the derived solutions remained disparate owing to the intensity provided, thus improving the overall process. The experimental results confirmed the effectiveness and superiority of the model using data from the 2008 Wenchuan earthquake. The ordinary NSGA-II and improved GE-NSGA-II algorithms were compared for optimal scheduling in three different post-earthquake rescue scenarios. In scenario 1, the total flight distance decreased by 23.04%, whereas satisfaction increased by 5.98%, showing the most significant optimization effect. In all three scenarios, with the increased scale of rescue operations and resource allocation complexity, the total flight distance increased by 210.00%, whereas satisfaction decreased from 0.855 1 to 0.611 5. Sensitivity analysis of parameters was based on population size, crossover probability, and mutation probability. [Conclusions] The findings of this study show that the proposed framework ensures that critically injured individuals receive priority search and rescue coverage in disaster scenarios. Moreover, the framework can dynamically adapt to continuously evolving operational requirements. The flexibility of a cooperative system is characterized by the aircraft's ability to allocate tasks according to its performance profile. For example, UAVs can be used effectively for clustered assessment missions in enemy zones, whereas helicopters can perform long-range heavy-lift operations. The results of this in-depth comparison show that the proposed algorithm is better than the traditional optimization algorithms at achieving the quality of the generated task allocation schemes and promoting maximum efficiency in resource utilization and timely rescue. This study provides a scientific decision-support framework for rescue commanders to coordinate the dispatch of heterogeneous aerial assets. The operational efficiency is greatly improved during the crucial golden rescue time. This study has immediate applications in earthquake or disaster response. In addition, it can be useful for more general coordination problems involving complex multiagents arising in other emergency situations that require dynamic allocation of heterogeneous resources, and responses must occur under severe constraints and in a time-critical environment.
关键词
震后场景 /
协同救援 /
异构航空器 /
多目标优化 /
救援任务分配 /
非支配排序遗传算法
Key words
post-earthquake scenarios /
collaborative rescue /
heterogeneous aircraft /
multi-objective optimization /
rescue task allocation /
non-dominated sorting genetic algorithm
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 樊海刚, 郭红梅, 张莹, 等. 随机Petri网在地震应急协同能力定量评价与动态分析中的应用[J]. 中国地震, 2022, 38(2): 248-259. FAN H G, GUO H M, ZHANG Y, et al. Application of stochastic Petri net in quantitative evaluation and dynamic analysis of earthquake emergency coordination capability[J]. Earthquake Research in China, 2022, 38(2): 248-259. (in Chinese)
[2]栗婧, 朱经政, 申彤. 面向公众的地震应激水平与影响因素评估[J]. 清华大学学报(自然科学版), 2024, 64(11): 1893-1901. LI J, ZHU J Z, SHEN T. Assessment of earthquake stress levels and influencing factors for the public[J]. Journal of Tsinghua University (Science and Technology), 2024, 64(11): 1893-1901. (in Chinese)
[3] 王天骄, 李松蔚, 李红军, 等. 基于启发式算法的直升机优化调度方法[J]. 信息与电脑, 2019(15): 41-42. WANG T J, LI S W, LI H J, et al. Helicopter optimization scheduling method based on heuristic algorithm[J]. China Computer & Communication, 2019(15): 41-42. (in Chinese)
[4] 高凯, 耿娜, 张馨月, 等. 基于PSO的载荷受限的灾后救援任务分配问题研究[J]. 重庆师范大学学报(自然科学版), 2021, 38(4): 10-20. GAO K, GENG N, ZHANG X Y, et al. Solving the task allocation problem of load-constrained after disaster based on PSO algorithm[J]. Journal of Chongqing Normal University (Natural Science), 2021, 38(4): 10-20. (in Chinese)
[5] 夏正洪, 潘卫军. 多救援直升机多目标分配与航迹规划研究[J]. 科学技术与工程, 2013, 13(34): 10226-10231. XIA Z H, PAN W J. Research on the multi-objective distribution of multi-helicopter and track planning[J]. Science Technology and Engineering, 2013, 13(34): 10226-10231. (in Chinese)
[6] GHAMRY K A, KAMEL M A, ZHANG Y M. Multiple UAVs in forest fire fighting mission using particle swarm optimization[C]// 2017 International Conference on Unmanned Aircraft Systems. Miami, USA: IEEE, 2017: 1404-1409.
[7]张祥银, 夏爽, 张天. 基于自适应遗传学习粒子群算法的多无人机协同任务分配[J]. 控制与决策, 2023, 38(11): 3103-3111. ZHANG X Y, XIA S, ZHANG T. Adaptive genetic learning particle swarm optimization based cooperative task allocation for multi-UAVs[J]. Control and Decision, 2023, 38(11): 3103-3111. (in Chinese)
[8] LIU H T, GE J Y, WANG Y L, et al. Multi-UAV optimal mission assignment and path planning for disaster rescue using adaptive genetic algorithm and improved artificial bee colony method[J]. Actuators, 2021, 11(1): 1-30.
[9] CHANDRAN I, VIPIN K. Multi-UAV networks for disaster monitoring: Challenges and opportunities from a network perspective[J]. Drone Systems and Applications, 2024, 12: 1-28.
[10] LEE H S, YOO H W, LEE B H. Deployment method of UAVs with energy constraint for multiple tasks[J]. Electronics Letters, 2015, 51(21): 1650-1652.
[11] TANG X, CHEN Q, WENG W J, et al. Task assignment and exploration optimization for low altitude UAV rescue via generative AI enhanced multi-agent reinforcement learning[J]. IEEE Transactions on Mobile Computing, 2026, 25(1): 627-643.
[12] 刘全义, 胡茂绮, 艾洪舟, 等. 突发事件下的空地联运协同调度问题综述[J]. 计算机应用与软件, 2024, 41(2): 1-7. LIU Q Y, HU M Q, AI H Z, et al. An overview of air-ground intermodal cooperative scheduling problem under emergencies[J]. Computer Applications and Software, 2024, 41(2): 1-7. (in Chinese)
[13] LI M C, GAO Y, WANG M L, et al. Multi-objective optimization for multi-task allocation in mobile crowd sensing[J]. Procedia Computer Science, 2019, 155: 360-368.
[14] TRIGUI S, CHEIKHROUHOU O, KOUBAA A, et al. FL-MTSP: A fuzzy logic approach to solve the multi-objective multiple traveling salesman problem for multi-robot systems[J]. Soft Computing, 2017, 21(24): 7351-7362.
[15] LI J, XIONG Y H, SHE J H, et al. A path planning method for sweep coverage with multiple UAVs[J]. IEEE Internet of Things Journal, 2020, 7(9): 8967-8978.
[16] REN Y P, TIAN G D, ZHOU M C. Scheduling of rescue vehicles to forest fires via multi-objective particle swarm optimization[C]// 2015 International Conference on Advanced Mechatronic Systems (ICAMechS). Beijing, China: IEEE, 2015: 79-85.
[17] NING Q, TAO G P, CHEN B C, et al. Multi-UAVs trajectory and mission cooperative planning based on the Markov model[J]. Physical Communication, 2019, 35: 100717.
[18] WU X L, YIN Y N, XU L, et al. Multi-UAV task allocation based on improved genetic algorithm[J]. IEEE Access, 2021, 9: 100369-100379.
[19] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transac- tions on Evolutionary Computation, 2002, 6(2): 182-197.
[20] 陈三燕, 王学武, 王烨, 等. 多种群混合进化算法求解带工序跳跃的分布式异构批量流混合流水车间调度问题[J]. 华东理工大学学报(自然科学版), 2025, 51(2): 228-241. CHEN S Y, WANG X W, WANG Y, et al. Multiple- population hybrid evolutionary algorithm for solving distributed heterogeneous lot-streaming hybrid flowshop scheduling problem with missing operations[J]. Journal of East China University of Science and Technology, 2025, 51(2): 228-241. (in Chinese)
[21] MA Y, LI B, HUANG W T, et al. An improved NSGA-II based on multi-task optimization for multi-UAV maritime search and rescue under severe weather[J]. Journal of Marine Science and Engineering, 2023, 11(4): 781.
[22] ZHU Y C, LIANG Y G, XIE Z Y, et al. Multi-type task assignment algorithm for heterogeneous UAV cluster based on improved NSGA-II[M]//YAN L, DUAN H B, DENG Y M. Advances in guidance, navigation and control. Singapore: Springer, 2025: 554-567.
[23] RABBANI M, OLADZAD-ABBASABADY N, AKBARIAN-SARAVI N. Ambulance routing in disaster response considering variable patient condition: NSGA-II and MOPSO algorithms[J]. Journal of Industrial and Management Optimization, 2022, 18(2): 1035-1062.
[24] 李亚飞, 赵瑞. 基于噪声与油耗的超声速客机进近程序优化[J]. 航空学报, 2025, 46(20): 531919. LI Y F, ZHAO R. Optimization of supersonic passenger aircraft approach procedure based on noise and fuel consumption[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(20): 531919. (in Chinese)
[25] DUAN P, YU Z A, GAO K Z, et al. Solving the multi-objective path planning problem for mobile robot using an improved NSGA-II algorithm[J]. Swarm and Evolutionary Computation, 2024, 87: 101576.
基金
四川省科技厅省院省校科技合作重点研发项目(2024YFHZ0027); 中央高校基本科研业务费专项资金项目(25CAFUC04084, 25CAFUC01007); 民航应急科学与技术重点实验室项目(NJ2022022, NJ2023025)
PDF(5663 KB)
