无人机配送研究：关于技术、效益及应用的系统综述

doi:10.16097/j.cnki.1009-6744.2025.06.004

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (6): 34-49.DOI: 10.16097/j.cnki.1009-6744.2025.06.004

• 智能交通系统与信息技术 • 上一篇下一篇

无人机配送研究：关于技术、效益及应用的系统综述

伍景琼^*a,b ，奠然^a,b ，字太升^a,b ，李云起^a,b

昆明理工大学，a.交通工程学院；b.云南省现代物流工程研究中心，昆明 650500

收稿日期:2025-07-04 修回日期:2025-08-21 接受日期:2025-08-28 出版日期:2025-12-25 发布日期:2025-12-23
作者简介:伍景琼(1984—)，女，湖南新化人，副教授，博士。
基金资助:
云南省“兴滇英才支持计划”青年人才项目/(XDYC-QNRC-2023-0122)；昆明理工大学创新发展研究基金专题项目(CFZH202405)。

Drone Delivery: A Systematic Review on Technology, Efficiency, and Applications

WUJingqiong^*a,b, DIAN Ran^a,b, ZI Taisheng^a,b, LI Yunqi^a,b

a. Faculty of Transportation Engineering; b. Yunnan Modern Logistics Engineering Research Center, Kunming University of Science and Technology, Kunming 650500, China

Received:2025-07-04 Revised:2025-08-21 Accepted:2025-08-28 Online:2025-12-25 Published:2025-12-23
Supported by:
Yunnan Revitalization Talent Support Program Young Project, China (XDYC-QNRC-2023-0122)；Innovation and Development Research Fund of Kunming University of Science and Technology, China (CFZH202405)。

摘要/Abstract

摘要： 随着电子商务快速发展和即时配送需求的激增，无人机配送作为物流领域的新型解决方案，正引发物流体系的深刻变革。本文系统回顾2015—2024年间发表的74篇相关文献，从关键技术、经济效益、环境可持续性、应用潜力和系统协同等维度，全面梳理无人机配送的研究进展。研究表明，无人机配送主要依赖路径规划算法、能源管理和多机协同等关键技术。相关优化研究已从单一目标优化向多目标协同演进，算法从经典启发式向智能算法发展，能有效减少求解时间和优化成本。但载重和风力的非线性影响，以及恶劣天气适应性仍是瓶颈。无人机配送在提升物流效率、降低配送成本和减少碳排放方面具有显著优势。在经济效益方面，无人机与车辆协同配送系统可通过优化路径规划和资源调度，显著缩短客户等待时间、降低配送成本及人力需求；与公共交通（公交/地铁）系统整合，能有效扩大覆盖服务范围并降低能耗；通过多目标优化模型，动态平衡能耗、成本与时效的关系，进一步提升协同效益；但其经济性受限于载重与航程，在短途轻载和应急货物配送更具优势。环境效益分析表明，无人机配送阶段的碳排放水平明显低于传统运输方式，但需综合考虑其全生命周期环境影响，包括制造、运营和回收等环节。应用层面，无人机配送在医疗物资配送、应急物流和城市“最后一公里”配送等领域展现出独特价值，在偏远地区和紧急场景下优势突出。然而，该技术仍面临安全风险、技术创新不足、社会认知局限和政策法规不完善等挑战。未来研究应聚焦电池技术突破、智能路径规划优化、隐私安全保护机制和跨区域政策协调，加速无人机配送的商业化应用进程。

关键词: 物流工程, 经济与环保效益, 系统综述, 无人机配送, 未来研究方向

Abstract: With the rapid development of e-commerce and a surge in demand for instant delivery, drone delivery, as an innovative solution in the logistics sector, is driving profound transformations in the logistics system. This paper synthesizes findings from 74 relevant articles published between 2015 and 2024, comprehensively examining drone delivery research advancements across key dimensions including critical technologies, economic benefits, environmental sustainability, application potential, and system synergy. The results indicate that drone delivery primarily relies on path planning algorithms, energy management, and multi-drone collaboration as its core technologies. Related optimization research has evolved from single-objective to multi-objective coordination, with algorithms transitioning from classical heuristics to intelligent approaches, effectively reducing solution time and optimizing costs. However, nonlinear effects of payload and wind resistance, along with adaptability to harsh weather conditions, remain bottlenecks. In terms of economic benefits, drone-vehicle collaborative systems can significantly reduce customer waiting time, delivery costs, and labor demands through optimized path planning and resource scheduling. Integration with public transit systems (bus/subway) effectively expands service coverage while reducing energy consumption. Multi-objective optimization models dynamically balance energy consumption, cost, and timeliness to further enhance synergistic benefits. Nevertheless, economic viability remains constrained by payload and range limitations, showing greater advantages in short distance, lightweight deliveries, particularly for emergency cargo deliveries. Environmental benefit analyses demonstrate that the operational phase of drone delivery exhibits significantly lower carbon emissions than traditional transportation methods, though a comprehensive lifecycle assessment encompassing manufacturing, operation, and recycling phases is required. Regarding applications, drone delivery technology demonstrates unique value in medical supply distribution, emergency logistics, and urban "last- mile" delivery, with particular advantages in remote areas and urgent emergency scenarios. However, challenges persist regarding safety risks, technological innovation gaps, limited social acceptance, and imperfect policy regulations. Future research should prioritize battery technology breakthroughs, intelligent path planning optimization, privacy/security safeguards, and cross regional policy coordination to accelerate drone delivery commercialization.

Key words: logistics engineering, economic and environmental benefits, systematic review, drone delivery, future research directions

中图分类号:

F252
V355

伍景琼, 奠然, 字太升, 李云起. 无人机配送研究：关于技术、效益及应用的系统综述[J]. 交通运输系统工程与信息, 2025, 25(6): 34-49.

WU Jingqiong, DIAN Ran, ZI Taisheng, LI Yunqi. Drone Delivery: A Systematic Review on Technology, Efficiency, and Applications[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(6): 34-49.

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参考文献

[1]MarketsandMarkets. World civil unmanned aerial systems: Market profile & forecast[R]. America: Finnegan Philip, 2023.

[2]MURRAY C C, CHU A G. The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery[J]. Transportation Research Part C: Emerging Technologies, 2015, 54: 86-109.

[3] RAIVI A M, HUDA S M A, ALAM M M, et al. Drone routing for drone-based delivery systems: A review of trajectory planning, charging, and security[J]. Sensors, 2023, 23(3): 1463.

[4]ESKANDARIPOUR H, BOLDSAIKHAN E. Last-mile drone delivery: Past, present, and future[J]. Drones, 2023, 7(2): 77.

[5]ZRELLI I, REJEB A, ABUSULAIMAN R, et al. Drone applications in logistics and supply chain management: A systematic review using latent dirichlet allocation[J]. Arabian Journal for Science and Engineering, 2024, 49 (9): 12411-12430.

[6]BAFOUNI-KOTTA A, VILLANUEVA J, KALAKOU S, et al. Upcoming drone delivery services: A perceptions based clustering of citizens in Lisbon[J]. Technology in Society, 2023, 73: 102250.

[7]ELSAYED M, FODA A, MOHAMED M. The impact of civil airspace policies on the viability of adopting autonomous unmanned aerial vehicles in last-mile applications[J]. Transport Policy, 2024, 145: 37-54.

[8]KITCHENHAM B, BRERETON O P, BUDGEN D, et al. Systematic literature reviews in software engineering: A systematic literature review[J]. Information and Software Technology, 2009, 51(1): 7-15.

[9] HAM A M. Integrated scheduling of m-truck, m-drone, and m-depot constrained by time-window, drop-pickup, and m-visit using constraint programming[J]. Transportation Research Part C: Emerging Technologies, 2018, 91: 1-14.

[10] RAJ R, MURRAY C. The multiple flying sidekicks traveling salesman problem with variable drone speeds [J]. Transportation Research Part C: Emerging Technologies, 2020, 120: 102813.

[11] 杜茂康,罗娟,李博文.基于多车场的车载无人机协同配送路径优化[J]. 系统工程,2021, 39(6): 90-98. [DU M K, LUO J, LI B W. Research on cooperative delivery route optimization of vehicle-carried drones based on multi-depot[J]. Systems Engineering, 2021, 39(6): 90 98.]

[12] SALAMA M R, SRINIVAS S. Collaborative truck multi drone routing and scheduling problem: Package delivery with flexible launch and recovery sites[J]. Transportation Research Part E: Logistics and Transportation Review, 2022, 164: 102788.

[13] 梁爽, 陈彦如,孙智彬.基于自适应大邻域搜索算法的无人机-卡车-代收点协同配送[J].工业工程与管理, 2024, 29(1): 119-132. [LIANG S, CHEN Y R, SUN Z B. An adaptive large neighborhood search for the drone truck-collection point collaborative delivery problem[J]. Industrial Engineering and Management, 2024, 29(1): 119-132.]

[14] 宋瑞, 边疆, 何世伟,等.考虑超重超远客户的卡车无人机协同配送研究[J]. 中国公路学报, 2024, 37(3): 395-406. [SONG R, BIAN J, HE S W, et al. Truck-drone joint delivery with consideration given to customers with great demands and at great distances[J]. China Journal of Highway and Transport, 2024, 37(3): 395-406.]

[15] DENG M, LI Y, DING J, et al. Stochastic and robust truck-and-drone routing problems with deadlines: A benders decomposition approach[J]. Transportation Research Part E: Logistics and Transportation Review, 2024, 190: 103709.

[16] SHE R, OUYANG Y. Hybrid truck-drone delivery under aerial traffic congestion[J]. Transportation Research Part B: Methodological, 2024, 185: 102970.

[17] 范厚明, 甘兰,张跃光,等.有禁飞区的时间依赖型车辆与无人机协同配送路径优化[J].控制理论与应用, 2024, 41(2): 321-330. [FAN H M, GAN L, ZHANG Y G, et al. Time-dependent vehicle routing problem with drones considering no-fly zones[J]. Control Theory & Applications, 2024, 41(2): 321-330.]

[18] SALEU R G M, DEROUSSI L, FEILLET D, et al. The parallel drone scheduling problem with multiple drones and vehicles[J]. European Journal of Operational Research, 2022, 300(2): 571-589.

[19] NGUYEN M A, DANG G T H, HA M H, et al. The min cost parallel drone scheduling vehicle routing problem [J]. European Journal of Operational Research, 2022, 299 (3): 910-930.

[20] 彭勇, 张永辉,黎元钧.考虑无人机辅助的卡车配送路径优化[J]. 工业工程与管理, 2023, 28(2): 31-39. [PENG Y, ZHANG Y H, LI Y J. Optimization of truck distribution route considering drone-assisted[J]. Industrial Engineering and Management, 2023, 28(2): 31-39.]

[21] LIU W, LIU L, QI X. Drone resupply with multiple trucks and drones for on-time delivery along given truck routes [J]. European Journal of Operational Research, 2024, 318 (2): 457-468.

[22] PINA-PARDO J C, SILVA D F, SMITH A E, et al. Dynamic vehicle routing problem with drone resupply for same-day delivery[J]. Transportation Research Part C: Emerging Technologies, 2024, 162: 104611.

[23] 林驿, 吕靖,蒋永雷.考虑交通时变特性的城乡快递无人机配送优化研究[J].计算机应用研究,2020,37(10): 2984-2989, 3013. [LIN Y, LV J, JIANG Y L. Research on optimization of drone delivery based on urban-rural transportation considering time-varying characteristics of traffic[J]. Application Research of Computers, 2020, 37 (10): 2984-2989, 3013.]

[24] CUI S, YANG Y, GAO K, et al. Integration of UAVs with public transit for delivery: Quantifying system benefits and policy implications[J]. Transportation Research Part A: Policy and Practice, 2024, 183: 104048.

[25] DENG T, XU X, ZOU Z, et al. Multidrone parcel delivery via public vehicles: A joint optimization approach[J]. IEEE Internet of Things Journal, 2023, 11(6): 9312 9323.

[26] HONG I, KUBY M, MURRAY A T. A range-restricted recharging station coverage model for drone delivery service planning[J]. Transportation Research Part C: Emerging Technologies, 2018, 90: 198-212.

[27] PINTO R, LAGORIO A. Point-to-point drone-based delivery network design with intermediate charging stations[J]. Transportation Research Part C: Emerging Technologies, 2022, 135: 103506.

[28] CHEN Z, HU Z, BAO Z, et al. UAV charging station planning and route optimization considering stochastic delivery demand[J]. IEEE Transactions on Transportation Electrification, 2024, 10(4): 9328-9341.

[29] PAN Y, CHEN Q, ZHANG N, et al. Extending delivery range and decelerating battery aging of logistics UAVs using public buses[J]. IEEE Transactions on Mobile Computing, 2022, 22(9): 5280-5295.

[30] 王莉莉, 欧俊杰.考虑电池充电的无人机车辆物流配送模型[J]. 科学技术与工程, 2024, 24(27): 11834 11841. [WANG L L, OU J J. Vehicle-mounted drones logistics distribution model considering battery charging [J]. Science Technology and Engineering, 2024, 24(27): 11834-11841.]

[31] DORLING K, HEINRICHS J, MESSIER G G, et al. Vehicle routing problems for drone delivery[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(1): 70-85.

[32] LIU B, NI W, ZHU H. Optimal charging scheduling and speed control for delay-bounded drone delivery[J]. IEEE Transactions on Vehicular Technology, 2024, 73(11): 16481-16490.

[33] PEI Z, PAN W, WENG K, et al. A branch-and-price-and cut algorithm for the unmanned aerial vehicle delivery with battery swapping[J]. International Journal of Production Research, 2024, 62(19): 7030-7055.

[34] 张洪海, 张连东,刘皞,等.城市低空物流无人机航迹规划模型研究[J].交通运输系统工程与信息,2022,22 (1): 256-264. [ZHANG H H, ZHANG L D, LIU H, et al. Track planning model for logistics unmanned aerial vehicle in urban low-altitude airspace[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 256-264.]

[35] SORBELLI F B, CORO F, DAS S K, et al. Energy constrained delivery of goods with drones under varying wind conditions[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(9): 6048-6060.

[36] 赵桂红, 秦伟祥.考虑天气因素的车辆联合无人机配送路径规划[J]. 综合运输, 2023, 45(9): 148-155. [ZHAO G H, QIN W X. Vehicle joint UAV distribution path planning under weather factors[J]. China Transportation Review, 2023, 45(9): 148-155.]

[37] AHMADI M, ZEGORDI S H. A novel mathematical model and a hybrid grouping evolution strategy algorithm for an automated last mile delivery system considering wind effect[J]. Engineering Applications of Artificial Intelligence, 2024, 127: 107363.

[38] SONG B D, PARK K, KIM J. Persistent UAV delivery logistics: MILP formulation and efficient heuristic[J]. Computers & Industrial Engineering, 2018, 120: 418 428.

[39] CAVANI S, IORI M, ROBERTI R. Exact methods for the traveling salesman problem with multiple drones[J]. Transportation Research Part C: Emerging Technologies, 2021, 130: 103280.

[40] ÖZBAYGIN T G, KARASAN O E, KARA B Y, et al. Exact solution approaches for the minimum total cost traveling salesman problem with multiple drones[J]. Transportation Research Part B: Methodological, 2023, 168: 81-123.

[41] LIU Z, LI X, KHOJANDI A. The flying sidekick traveling salesman problem with stochastic travel time: A reinforcement learning approach[J]. Transportation Research Part E: Logistics and Transportation Review, 2022, 164: 102816.

[42] XIA Y, ZENG W, ZHANG C, et al. A branch-and-price and-cut algorithm for the vehicle routing problem with load-dependent drones[J]. Transportation Research Part B: Methodological, 2023, 171: 80-110.

[43] 郭兴海, 计明军,温都苏,等.“最后一公里”配送的分布式多无人机的任务分配和路径规划[J].系统工程理论与实践,2021, 41(4): 946-961. [GUO X H, JI M J, WEN D S, et al. Task assignment and path planning for distributed multiple unmanned aerial vehicles in the "last mile" [J]. Systems Engineering-Theory & Practice, 2021, 41(4): 946-961.]

[44] LIU B, NI W, LIU R P, et al. Decentralized, privacy preserving routing of cellular-connected unmanned aerial vehicles for joint goods delivery and sensing[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(9): 9627-9641.

[45] HE X, LI L, MO Y, et al. A distributed route network planning method with congestion pricing for drone delivery services in cities[J]. Transportation Research Part C: Emerging Technologies, 2024, 160: 104536.

[46] MOSHREF-JAVADI M, LEE S, WINKENBACH M. Design and evaluation of a multi-trip delivery model with truck and drones[J]. Transportation Research Part E: Logistics and Transportation Review, 2020, 136: 101887.

[47] 季金华, 刘亚君,别一鸣,等.基于无人机与卡车协作的封控社区生活物资配送方法[J].交通运输系统工程与信息,2022, 22(5): 264-272. [JI J H, LIU Y J, BIE Y M, et al. Delivery method of living goods in controlled communities based on cooperation between drones and truck[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 264-272.]

[48] CHEN E, ZHOU Z, LI R, et al. The multi-fleet delivery problem combined with trucks, tricycles, and drones for last-mile logistics efficiency requirements under multiple budget constraints[J]. Transportation Research Part E: Logistics and Transportation Review, 2024, 187: 103573.

[49] ZHANG J, CAMPBELL J F, SWEENEY II D C, et al. Energy consumption models for delivery drones: A comparison and assessment[J]. Transportation Research Part D: Transport and Environment, 2021, 90: 102668.

[50] MENG Z, ZHOU Y, LI E Y, et al. Environmental and economic impacts of drone-assisted truck delivery under the carbon market price[J]. Journal of Cleaner Production, 2023, 401: 136758.

[51] KIRSCHSTEIN T. Comparison of energy demands of drone-based and ground-based parcel delivery services [J]. Transportation Research Part D: Transport and Environment, 2020, 78: 102209.

[52] FIGLIOZZI M A. Lifecycle modeling and assessment of unmanned aerial vehicles (Drones) CO2 emissions[J]. Transportation Research Part D: Transport and Environment, 2017, 57: 251-261.

[53] KOIWANIT J. Analysis of environmental impacts of drone delivery on an online shopping system[J]. Advances in Climate Change Research, 2018, 9(3): 201 207.

[54] BALDISSERI A, SIRAGUSA C, SEGHEZZI A, et al. Truck-based drone delivery system: An economic and environmental assessment[J]. Transportation Research Part D: Transport and Environment, 2022, 107: 103296.

[55] GOODCHILD A, TOY J. Delivery by drone: An evaluation of unmanned aerial vehicle technology in reducing CO2 emissions in the delivery service industry [J]. Transportation Research Part D: Transport and Environment, 2018, 61: 58-67.

[56] KWASIBORSKA A, STELMACH A, JABLONSKA I. Quantitative and comparative analysis of energy consumption in urban logistics using unmanned aerial vehicles and selected means of transport[J]. Energies, 2023, 16(18): 6467.

[57] 蒋丽, 杨露, 梁昌勇,等.基于无人机的高层住宅最后“一百米”配送优化[J]. 交通运输系统工程与信息, 2022, 22(4): 236-245, 292. [JIANG L, YANG L, LIANG C Y, et al. Optimization of final "100 Meters" drone delivery in high-rise residential buildings[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(4): 236-245, 292.]

[58] EZAKI T, FUJITSUKA K, IMURA N, et al. Drone-based vertical delivery system for high-rise buildings: Multiple drones vs. a single elevator[J]. Communications in Transportation Research, 2024, 4: 100130.

[59] 李翰, 张洪海,张连东,等.城市区域多物流无人机协同任务分配[J]. 系统工程与电子技术,2021, 43(12): 3594-3602. [LI H, ZHANG H H, ZHANG L D, et al. Multiple logistics unmanned aerial vehicle collaborative task allocation in urban areas[J]. Systems Engineering and Electronics, 2021, 43(12): 3594-3602.]

[60] ZHANG S, LIU S, XU W, et al. A novel multi-objective optimization model for the vehicle routing problem with drone delivery and dynamic flight endurance[J]. Computers & Industrial Engineering, 2022, 173: 108679.

[61] 张洪海, 冯棣坤,张晓玮,等.城市物流无人机起降点布局规划研究[J].交通运输系统工程与信息,2022,22 (3): 207-214. [ZHANG H H, FENG D K, ZHANG X W, et al. Urban logistics unmanned aerial vehicle vertiports layout planning[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 207-214.]

[62] KARAK A, ABDELGHANY K. The hybrid vehicle-drone routing problem for pick-up and delivery services[J]. Transportation Research Part C: Emerging Technologies, 2019, 102: 427-449.

[63] 柳伍生,李旺,周清,等.“无人机-车辆”配送路径优化模型与算法[J]. 交通运输系统工程与信息,2021,21 (6): 176-186. [LIU W S, LI W, ZHOU Q, et al. "Drone vehicle" distribution routing optimization model[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(6): 176-186.]

[64] ZANG X, JIANG L, LIANG C, et al. Optimization approaches for the urban delivery problem with trucks and drones[J]. Swarm and Evolutionary Computation, 2022, 75: 101147.

[65] GAO J, ZHEN L, LAPORTE G, et al. Scheduling trucks and drones for cooperative deliveries[J]. Transportation Research Part E: Logistics and Transportation Review, 2023, 178: 103267.

[66] 魏明, 孙雅茹,孙博,等.基于深度强化学习的无人机线路及航迹协同规划[J].中国安全科学学报,2023,33 (8): 68-76. [WEI M, SUN Y R, SUN B, et al. UAV distribution route and flight path collaborative planning based on deep reinforcement learning[J]. China Safety Science Journal, 2023, 33(8): 68-76.]

[67] KONG F, JIANG B. Delivery optimization for collaborative truck-drone routing problem considering vehicle obstacle avoidance[J]. Computers & Industrial Engineering, 2024, 198: 110659.

[68] HAIDARI L A, BROWN S T, FERGUSON M, et al. The economic and operational value of using drones to transport vaccines[J]. Vaccine, 2016, 34(34): 4062-4067.

[69] GARG V, NIRANJAN S, PRYBUTOK V, et al. Drones in last-mile delivery: A systematic review on efficiency, accessibility, and sustainability[J]. Transportation Research Part D: Transport and Environment, 2023, 123: 103831.

[70] DOOLE M, ELLERBROEK J, KNOOP V L, et al. Constrained urban airspace design for large-scale drone based delivery traffic[J]. Aerospace, 2021, 8(2): 38.

[71] AJAKWE S O, KIM D S, LEE J M. Drone transportation system: Systematic review of security dynamics for smart mobility[J]. IEEE Internet of Things Journal, 2023, 10 (16): 14462-14482.

[72] YOO W, YU E, JUNG J. Drone delivery: Factors affecting the public's attitude and intention to adopt[J]. Telematics and Informatics, 2018, 35(6): 1687-1700.

[73] BERKE A, DING G, CHIN C, et al. Drone delivery and the value of customer privacy: A discrete choice experiment with US consumers[J]. Transportation Research Part C: Emerging Technologies, 2023, 157: 104391.

[74] HWANG J, KIM H. Consequences of a green image of drone food delivery services: The moderating role of gender and age[J]. Business Strategy and the Environment, 2019, 28(5): 872-884.

[75] LI X, LEE G J X, YUEN K F. Consumer acceptance of urban drone delivery: The role of perceived anthropomorphic characteristics[J]. Cities, 2024, 148: 104867.

[76] ELSAYED M, MOHAMED M. The impact of airspace regulations on unmanned aerial vehicles in last-mile operation[J]. Transportation Research Part D: Transport and Environment, 2020, 87: 102480.

[77] NIU B, ZHANG J, XIE F. Drone logistics' resilient development: Impacts of consumer choice, competition, and regulation[J]. Transportation Research Part A: Policy and Practice, 2024, 185: 104126.

无人机配送研究：关于技术、效益及应用的系统综述

Drone Delivery: A Systematic Review on Technology, Efficiency, and Applications

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[2]	程佳豪, 郝志丹, 李国旗, 刘思婧. 卡车-冲锋舟协同的山区洪灾应急物资配送多目标优化[J]. 交通运输系统工程与信息, 2025, 25(6): 294-304.
[3]	陈俊熙, 卫振林. 多移动中转站下货车-多机器人协同配送路径优化[J]. 交通运输系统工程与信息, 2025, 25(6): 305-316.
[4]	唐炜琳, 郎茂祥, 陈星瀚. 考虑灵活充电的零担快运自动化越库作业设备联合调度[J]. 交通运输系统工程与信息, 2025, 25(6): 327-340.
[5]	刘长石, 万城, 王凤, 陈宝玺, 岳俊羽. 基于救援效用的洪灾物资配送与灾民转移路径规划[J]. 交通运输系统工程与信息, 2025, 25(5): 320-332.
[6]	刘长石, 万城, 王凤, 刘光洪, 陈宝玺. 洪涝灾害下考虑受灾点差异的应急物资分配-配送优化[J]. 交通运输系统工程与信息, 2025, 25(3): 335-345.
[7]	栾建霖, 游蕊齐, 王斯妮, 贾鹏. 网络抽象视角下干线送达时间可靠度无参估计方法[J]. 交通运输系统工程与信息, 2025, 25(2): 138-145.
[8]	李国旗, 郝志丹, 杨佳鑫, 程佳豪. 运力短缺下的生活物资临时分配点选址-路径优化研究[J]. 交通运输系统工程与信息, 2025, 25(2): 304-313.
[9]	杨华龙, 石兴江, 辛禹辰. 全渠道电商库存路径及定价问题优化研究[J]. 交通运输系统工程与信息, 2024, 24(4): 223-230.
[10]	何美玲, 杨梅, 韩珣, 武晓晖. 带时间窗的时间依赖型同时取送货车辆路径问题研究[J]. 交通运输系统工程与信息, 2024, 24(4): 231-242.
[11]	孙文杰, 张锦, 刘娇, 李国旗. 客车代运与送提一体下的山区农村寄递网络优化[J]. 交通运输系统工程与信息, 2024, 24(3): 94-102.
[12]	许波桅, 范会尧, 李军军. 基于势博弈的自动化码头路网流量均衡研究[J]. 交通运输系统工程与信息, 2024, 24(3): 255-264.
[13]	刘新全, 汪鑫羽, 黄英艺. 基于用户偏好行为的物流枢纽网络负载均衡优化[J]. 交通运输系统工程与信息, 2024, 24(3): 265-276.
[14]	宋丽英, 赵世超, 卞骞, 杜鹏, 沈鹏举. 低碳视角下城乡区域混合车队生鲜配送路径问题研究[J]. 交通运输系统工程与信息, 2023, 23(6): 250-261.
[15]	刘永, 岳志城, 王勇. 托盘装载约束下带时间窗的配送车辆路径优化研究[J]. 交通运输系统工程与信息, 2023, 23(6): 262-273.