城市低空立体物流网络双种群协同优化方法
收稿日期: 2024-10-31
修回日期: 2024-12-09
录用日期: 2025-03-03
网络出版日期: 2025-03-12
基金资助
北航科研项目基金(23100002022102001);国家自然科学基金(U2333218);国家自然科学基金(52302398);国家自然科学基金(61827901);北京市自然科学基金(L241036)
Dual-population coevolutionary optimization for multi-layer urban air logistics network
Received date: 2024-10-31
Revised date: 2024-12-09
Accepted date: 2025-03-03
Online published: 2025-03-12
Supported by
Beihang Research Project(23100002022102001);National Natural Science Foundation of China(U2333218);Beijing Natural Science Foundation(L241036)
城市无人机（UAV）物流是低空经济落地应用的重要场景，城市低空物流（UAL）网络是其关键基础设施。综合考虑噪声约束、经济成本和对地安全风险等因素，开展了城市低空物流网络优化方法研究。在噪声约束下，以最小化经济成本和对地安全风险为优化目标，建立了多目标混合整数规划模型，提出了一种双种群协同进化优化求解算法，通过种群间的个体交互实现知识迁移，有效提升了算法在不规则解空间中的寻优能力。实验表明，本方法与现有方法相比，寻优性能平均提高20%以上，所设计的多高度层立体网络达到了成本、对地安全风险、噪声的均衡最优。
张春晓 , 郭通 , 李宇萌 . 城市低空立体物流网络双种群协同优化方法[J]. 航空学报, 2025 , 46(11) : 531477 -531477 . DOI: 10.7527/S1000-6893.2024.31477
Urban Unmanned Aerial Vehicle （UAV） logistics is a significant application for the low-altitude economy， and the Urban Air Logistics （UAL） network is a critical infrastructure for achieving efficient drone delivery. This paper comprehensively considers critical urban factors such as noise constraints， economic costs， and ground safety risks to investigate optimization methodologies for urban air logistics networks. We propose a novel multiobjective mixed-integer programming model that simultaneously minimizes operational costs and ground safety risks while strictly under noise constraints. A dual-population coevolutionary optimization algorithm is developed， which enables knowledge transfer through individual interactions between populations， effectively enhancing the optimization capability of the algorithm in irregular solution spaces. Computational experiments show that the proposed algorithm outperforms existing methods with performance improving by over 20% on average. The designed multi-layered network achieves a balanced optimum in terms of cost， ground safety risk， and noise.
| [1] | DU W B， GUO T， CHEN J， et al. Cooperative pursuit of unauthorized UAVs in urban airspace via Multi-agent reinforcement learning［J］. Transportation Research Part C： Emerging Technologies， 2021， 128： 103122. |
| [2] | XING J H， SU L C， HONG W J， et al. Aerial-ground collaborative routing with time constraints［J］. Chinese Journal of Aeronautics， 2023， 36（2）： 270-283. |
| [3] | GARROW L A， GERMAN B J， LEONARD C E. Urban air mobility： A comprehensive review and comparative analysis with autonomous and electric ground transportation for informing future research［J］. Transportation Research Part C： Emerging Technologies， 2021， 132： 103377. |
| [4] | RIFAN R， ADIKARIWATTAGE V， DE BARROS A. Identification of urban air logistics distribution network concepts［J］. Transportation Research Record： Journal of the Transportation Research Board， 2023， 2677（2）： 129-153. |
| [5] | KELLERMANN R， BIEHLE T， FISCHER L. Drones for parcel and passenger transportation： A literature review［J］. Transportation Research Interdisciplinary Perspectives， 2020， 4： 100088. |
| [6] | HOU W J， FANG T， PEI Z， et al. Integrated design of unmanned aerial mobility network： A data-driven risk-averse approach［J］. International Journal of Production Economics， 2021， 236： 108131. |
| [7] | FARAZI N P， ZOU B. Planning electric vertical takeoff and landing aircraft （eVTOL）?-based package delivery with community noise impact considerations［J］. Transportation Research Part E： Logistics and Transportation Review， 2024， 189： 103661. |
| [8] | PARK Y， LEE S， SUNG I， et al. Facility location-allocation problem for emergency medical service with unmanned aerial vehicle［J］. IEEE Transactions on Intelligent Transportation Systems， 2023， 24（2）： 1465-1479. |
| [9] | ZHU T K， BOYLES S D， UNNIKRISHNAN A. Two-stage robust facility location problem with drones?［J］. Transportation Research Part C： Emerging Technologies， 2022， 137： 103563. |
| [10] | 任新惠， 王柳， 王佳雪. 基于分区优化的无人机全自动机场选址研究［J］. 运筹与管理， 2023， 32（6）： 20-26. |
| REN X H， WANG L， WANG J X. Automatic vertiport location of unmanned aerial vehicle based on partition optimization［J］. Operations Research and Management Science， 2023， 32（6）： 20-26 （in Chinese）. | |
| [11] | ZHANG C X， DU W B， GUO T， et al. Multi-objective hub location for urban air mobility via self-adaptive evolutionary algorithm［J］. Advanced Engineering Informatics， 2025， 64： 102974. |
| [12] | SUN X T， LI X H. A drone-driven delivery network design for an on-demand O2O platform considering hazard risks and customer heterogeneity［J］. Asia-Pacific Journal of Operational Research， 2024， 41（4）： 2440004. |
| [13] | Bulusu V， Polishchuk V， Sedov L. Noise Estimation for future large-scale small UAS Operations［C］?∥INTER-NOISE and NOISE-CON Congress and Conference Proceedings. Wakefield： Institute of Noise Control Engineering， 2017， 254（2）： 864-871. |
| [14] | SCH?FFER B， PIEREN R， HEUTSCHI K， et al. Drone noise emission characteristics and noise effects on humans-a systematic review［J］. International Journal of Environmental Research and Public Health， 2021， 18（11）： 5940. |
| [15] | TORIJA A J， LI Z G， SELF R H. Effects of a hovering unmanned aerial vehicle on urban soundscapes perception［J］. Transportation Research Part D： Transport and Environment， 2020， 78： 102195. |
| [16] | EASA N N. Study on the societal acceptance of urban air mobility in Europe［J］. 2021. |
| [17] | HADDAD C AL， CHANIOTAKIS E， STRAUBINGER A， et al. Factors affecting the adoption and use of urban air mobility［J］. Transportation Research Part A： Policy and Practice， 2020， 132： 696-712. |
| [18] | TAN Q C， HOU J F， LI Y H， et al. Exploring noise reduction strategies： Optimizing drone station placement for last-mile delivery［J］. Transportation Research Part D： Transport and Environment， 2024， 133： 104306. |
| [19] | TAN Q C， BIAN H Y， GUO J W， et al. Virtual flight simulation of delivery drone noise in the urban residential community［J］. Transportation Research Part D： Transport and Environment， 2023， 118： 103686. |
| [20] | DEB K， PRATAP A， AGARWAL S， et al. A fast and elitist multiobjective genetic algorithm： NSGA-?Ⅱ?［J］. IEEE Transactions on Evolutionary Computation， 2002， 6（2）： 182-197. |
| [21] | 张洪海， 邹依原， 张启钱， 等. 未来城市空中交通管理研究综述［J］. 航空学报， 2021， 42（7）： 024638. |
| ZHANG H H， ZOU Y Y， ZHANG Q Q， et al. Future urban air mobility management： Review［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（7）： 024638 （in Chinese）. | |
| [22] | COHEN A P， SHAHEEN S A， FARRAR E M. Urban air mobility： History， ecosystem， market potential， and challenges［J］. IEEE Transactions on Intelligent Transportation Systems， 2021， 22（9）： 6074-6087. |
| [23] | 曲欣宇， 叶博嘉， 程予， 等. 物流无人机城市低空轴辐式网络构建方法研究［J］. 山东科学， 2023， 36（6）： 86-95. |
| QU X Y， YE B J， CHENG Y， et al. The method to construct an urban logistics unmanned aerial vehicles low-altitude hub-and-spoke network［J］. Shandong Science， 2023， 36（6）： 86-95 （in Chinese）. | |
| [24] | KARIMI H， SETAK M. Proprietor and customer costs in the incomplete hub location-routing network topology［J］. Applied Mathematical Modelling， 2014， 38（3）： 1011-1023. |
| [25] | PANG B Z， HU X T， DAI W， et al. UAV path optimization with an integrated cost assessment model considering third-party risks in metropolitan environments［J］. Reliability Engineering & System Safety， 2022， 222： 108399. |
| [26] | CHEN K S， LUO W J， LIN X， et al. Evolutionary biparty multiobjective UAV path planning： Problems and empirical comparisons［J］. IEEE Transactions on Emerging Topics in Computational Intelligence， 2024， 8（3）： 2433-2445. |
| [27] | JIANG C P， BLOM H A， SHARPANSKYKH A. Third party risk indicators and their use in safety regulations for UAS operations： AIAA-2020-2901［R］. Reston： AIAA， 2020. |
| [28] | PRIMATESTA S， GUGLIERI G， RIZZO A. A risk-aware path planning strategy for UAVs in urban environments?［J］. Journal of Intelligent & Robotic Systems， 2019， 95（2）： 629-643. |
| [29] | ALEXANDER W N， WHELCHEL J， INTARATEP N， et al. Predicting community noise of sUAS： AIAA-2019-2686［R］. Reston： AIAA， 2019. |
| [30] | GAO Z Y， YU Y， WEI Q S， et al. Noise-aware and equitable urban air traffic management： An optimization approach［DB/OL］. arXiv preprint： 2401.00806； 2024. |
| [31] | TIAN Y， ZHANG T， XIAO J H， et al. A coevolutionary framework for constrained multiobjective optimization problems［J］. IEEE Transactions on Evolutionary Computation， 2021， 25（1）： 102-116. |
| [32] | ZITZLER E. SPEA2： Improving the strength pareto evolutionary algorithm： ethz-a-004284029［R］. Zurich： ETH Zurich， 2001. |
| [33] | DEMIR ?， KIRAZ B， CORUT ERGIN F. Experimental evaluation of meta-heuristics for multi-objective capacitated multiple allocation hub location problem［J］. Engineering Science and Technology， an International Journal， 2022， 29： 101032. |
| [34] | GUIZZO G， FRITSCHE G M， VERGILIO S R， et al. A hyper-heuristic for the multi-objective integration and test order problem［C］∥ Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. New York： ACM， 2015： 1343-1350. |
| [35] | LI A， HANSEN M， ZOU B. Traffic management and resource allocation for UAV-based parcel delivery in low-altitude urban space［J］. Transportation Research Part C： Emerging Technologies， 2022， 143： 103808. |
| [36] | 成都市邮政管理局. 2019年成都市邮政行业发展统计公报［EB/OL］. （2020-5-29）［2024-10-31］. . |
| Chengdu Municipal Postal Administration. 2019 Chengdu postal industry development statistical bulletin［EB/OL］. （2020-5-29）［2024-10-31］. （in Chinese）. | |
| [37] | CHIANG W C， LI Y Y， SHANG J， et al. Impact of drone delivery on sustainability and cost： Realizing the UAV potential through vehicle routing optimization［J］. Applied Energy， 2019， 242： 1164-1175. |
| [38] | ERNST A T， KRISHNAMOORTHY M. Efficient algorithms for the uncapacitated single allocation p-hub Median problem［J］. Location Science， 1996， 4（3）： 139-154. |
| [39] | D’SOUZA S， ISHIHARA A， NIKAIDO B， et al. Feasibility of varying geo-fence around an unmanned aircraft operation based on vehicle performance and wind［C］?∥ 2016 IEEE/AIAA 35th Digital Avionics Systems Conference （DASC）. Piscataway： IEEE Press， 2016： 1-10. |
| [40] | 中华人民共和国生态环境部. 机场周围航空噪声监测技术规范（征求意见稿）［EB/OL］. （2024-4-12） ［2024-10-31］. . |
| Ministry of Ecology and Environment of the People’s Republic of China. Technical specifications for monitoring of aircraft noise in the vicinity of airports （Exposure draft）［EB/OL］. （2024-4-12） ［2024-10-31］. （in Chinese）. | |
| [41] | 中华人民共和国环境保护部. 《机场周围区域飞机噪声环境质量标准（二次征求意见稿）》编制说明［EB/OL］. （2017-11-17）［2024-10-31］. . |
| Ministry of Environmental Protection. Preparation Instructions for the “Environmental quality standard for airplane noise in the vicinity of airports （Second draft for comment）”［EB/OL］. （2017-11-17）［2024-10-31］. （in Chinese）. | |
| [42] | MRABTI N， HAMANI N， BOULAKSIL Y， et al. A multi-objective optimization model for the problems of sustainable collaborative hub location and cost sharing［J］. Transportation Research Part E： Logistics and Transportation Review， 2022， 164： 102821. |
| [43] | YANG X， BOSTEL N， DEJAX P. A MILP model and memetic algorithm for the Hub Location and Routing problem with distinct collection and delivery Tours［J］. Computers & Industrial Engineering， 2019， 135： 105-119. |
| [44] | ZHALECHIAN M， TAVAKKOLI-MOGHADDAM R， RAHIMI Y， et al. An interactive possibilistic programming approach for a multi-objective hub location problem： Economic and environmental design［J］. Applied Soft Computing， 2017， 52： 699-713. |
/
| 〈 |
|
〉 |
CJA