可持续航空燃料与机型和航线三维耦合优化方法

doi:10.16097/j.cnki.1009-6744.2026.02.016

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (2): 169-179.DOI: 10.16097/j.cnki.1009-6744.2026.02.016

可持续航空燃料与机型和航线三维耦合优化方法

李艳华，王越超，任广建^*，王涛，祁嘉仪

北京交通大学，交通运输学院，北京100044

收稿日期:2025-11-09 修回日期:2025-12-17 接受日期:2025-12-18 出版日期:2026-04-25 发布日期:2026-04-20
作者简介:李艳华（1969—），女，河南汝南人，教授，博士。
基金资助:
国家自然科学基金“民航联合基金”项目(U2333206)。

Integrated Optimization for Sustainable Aviation Fuel, Aircraft, and Routes

LI Yanhua, WANG Yuechao, REN Guangjian^*, WANG Tao, QI Jiayi

School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Received:2025-11-09 Revised:2025-12-17 Accepted:2025-12-18 Online:2026-04-25 Published:2026-04-20
Supported by:
Joint Funds of the National Natural Science Foundation of China(U2333206)。

摘要/Abstract

摘要： 可持续航空燃料（Sustainable Aviation Fuel, SAF）具有显著减排潜力，但面临成本高昂和产量有限的现实难题。为提高航司使用SAF的效益，本文提出SAF、航线与机型的三维耦合优化方法，综合考虑机型燃油效率，航线成本以及碳排放层面的差异，以航空公司直接运营成本与碳排放量最小为目标建立SAF差异化配比优化模型。针对目标和约束的双线性特征，采用三阶段线性化策略实现模型重构，并设计结合ε约束的丹齐格-沃尔夫（Dantzig-Wolfe,DW）分解算法求解模型。最后，选取中国前5大航空公司、40个干线机场网络及10种主力机型为案例，结合24万条航迹数据，展开实例验证。结果表明，结合ε-约束的DW分解算法在求解效率和精度上均优于Gurobi直接求解。在较低减排水平下，SAF被集中配置于新一代窄体机的中程航线中，占使用总量的76.1%，此时，整体减排效益最佳。随减排量增加，宽体机在长航程上的配比逐步提升，SAF使用占比从7.3%提升至27.1%。这种差异化SAF配比方法较统一配比模式降低了碳排放约3.0%~4.8%。航线特征、航班规模与机型结构共同影响机场和航司最佳SAF配比和减排贡献。灵敏度分析表明，SAF供应量的增加以及SAF价格的降低将进一步提升SAF差异化配比方法的减排效益。本文为航空公司如何科学调控SAF掺混比例和提升减排效益提供了定量参考。

关键词: 航空运输, 三维耦合优化方法, ε约束方法-Dantzig-Wolfe分解, 可持续航空燃料, 碳排放

Abstract: Sustainable aviation fuel (SAF) has substantial decarbonization potential, yet faces practical constraints including high costs and limited supply. To improve the cost-effectiveness of SAF adoption by airlines, this study develops a three-dimensional coupled optimization framework linking SAF allocation, route characteristics, and aircraft types. By considering fuel efficiency differences at the aircraft level and cost and carbon emission variations at the route level, the study proposes an SAF differentiated blending optimization model to minimize airline operating costs and carbon emissions. A three-stage linearization strategy is used to linearize the bilinear objective and constraints, and a Dantzig-Wolfe (DW) decomposition algorithm with ε-constraints is applied to solve the model. A case study using data from the top five Chinese airlines, 40 major airports, and 10 aircraft models, along with 240 000 flight trajectory data points, is conducted. The results show that the DW decomposition algorithm with ε-constraints outperforms direct solving with Gurobi in both efficiency and accuracy. At lower abatement levels, SAF is concentrated on medium-haul operations of new-generation narrow-body aircraft, accounting for 76.1% of total SAF use, where overall mitigation effectiveness is maximized. As the abatement target increases, SAF allocation to wide-body aircraft on long-haul routes rises, with their share of SAF use increasing to 27.1%. Compared with a uniform blending scheme, this differentiated SAF allocation reduces emissions by approximately 3.0%~4.8%. Route characteristics, flight scale, and fleet composition jointly shape the optimal CO2 SAF blending strategy and mitigation contributions for airports and airlines. Sensitivity analysis indicates that increasing SAF supply and reducing SAF prices will further enhance emission reduction benefits. This study provides a quantitative reference for airlines to optimize SAF blending ratios to improve emission reduction outcomes.

Key words: air transportation, tri-dimensional coupled optimization method, ε-constraint-Dantzig-Wolfe decomposition, sustainable aviation fuel(SAF), carbon emissions

中图分类号:

F562.6

李艳华, 王越超, 任广建, 王涛, 祁嘉仪. 可持续航空燃料与机型和航线三维耦合优化方法[J]. 交通运输系统工程与信息, 2026, 26(2): 169-179.

LI Yanhua, WANG Yuechao, REN Guangjian, WANG Tao, QI Jiayi. Integrated Optimization for Sustainable Aviation Fuel, Aircraft, and Routes[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(2): 169-179.

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链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2026.02.016

http://www.tseit.org.cn/CN/Y2026/V26/I2/169

参考文献

[1]RUPCIC L, PIERRAT E, SAAVEDRA-RUBIO K, et al. Environmental impacts in the civil aviation sector: Current state and guidance[J]. Transportation Research Part D: Transport and Environment, 2023, 119: 103717.

[2] CHIARAMONTI D, GOUMAS T. Impacts on industrial scale market deployment of advanced biofuels and recycled carbon fuels from the EU renewable energy directive II[J]. Applied Energy, 2019, 251: 113351.

[3]SUN X, BAUMEISTER S, MARTIN-DOMINGO L, et al. From hero to zero: Barriers to achieving IATA's 2050 sustainability targets[J]. Transport Policy, 2025, 174: 103830.

[4] 田利军,陈学功,王琦,等.中国民航可持续航空燃油减排潜力及多路径协同研究[J].交通运输系统工程与信息,2025, 25(5): 365-374. [TIAN L J, CHEN X G, WANG Q, et al. Emission reduction potential and multi path synergy of sustainable aviation fuel in China's civil aviation[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(5): 365-374.]

[5] 田利军,刘鑫,吕继兴,等.我国可持续航空燃料产业发展研究[J]. 中国工程科学,2025, 27(2): 49-61. [TIAN L J, LIU X, LV J X, et al. Development strategy of sustainable aviation fuel[J]. Chinese Engineering Science, 2025, 27 (2): 49-61.]

[6]OGUNTONA O. Longer-term aircraft fleet modelling: Narrative review of tools and measures for mitigating carbon emissions from aircraft fleet[J]. CEAS Aeronautical Journal, 2020, 11(1): 13-31.

[7] 韩博,邓志强,于敬磊,等.碳达峰目标下中国民航CO2 与NOx 减排协同效益分析[J].交通运输系统工程与信息, 2022, 22(4): 53-62. [HAN B, DENG Z Q, YU J L, et al. Synergistic benefit analysis of CO2 and NOx emissions in civil aviation of China under dual carbon target[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(4): 53-62.]

[8]HU Y J, YANG L, CUI H, et al. Developing a balanced strategy: A multi-objective model for emissions reduction and development of civil aviation in China[J]. Energy, 2024, 307: 132597.

[9] MÜLLER C, KIECKHÄFER K, SPENGLER T S. The influence of emission thresholds and retrofit options on airline fleet planning: An optimization approach[J]. Energy Policy, 2018, 112: 242-257.

[10] ABRANTES I, FERREIRA A F, SILVA A, et al. Sustainable aviation fuels and imminent technologies CO2 emissions evolution towards 2050[J]. Journal of Cleaner Production, 2021, 313: 127937.

[11] HAMDAN S, JOUINI O, CHEAITOU A, et al. Air traffic flow management under emission policies: Analyzing the impact of sustainable aviation fuel and different carbon prices[J]. Transportation Research Part A: Policy and Practice, 2022, 166: 14-40.

[12] FAWWAZ ALREBEI O, ALHERBAWI M, THIEHMED Z, et al. Aircraft performance of a novel SAF: Lower costs, lower environmental impact, and higher aircraft performance[J]. Energy Conversion and Management: X, 2024, 24: 100739.

[13] SEYMOUR K, HELD M, GEORGES G, et al. Fuel estimation in air transportation: Modeling global fuel consumption for commercial aviation[J]. Transportation Research Part D: Transport and Environment, 2020, 88: 102528.

[14] MITSOS A, CHACHUAT B, BARTON P I. McCormick based relaxations of algorithms[J]. SIAM Journal on Optimization, 2009, 20(2): 573-601.

[15] WANG B, TING Z J, ZHAO M. Sustainable aviation fuels: Key opportunities and challenges in lowering carbon emissions for aviation industry[J]. Carbon Capture Science & Technology, 2024, 13: 100263.

可持续航空燃料与机型和航线三维耦合优化方法

Integrated Optimization for Sustainable Aviation Fuel, Aircraft, and Routes

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