Collaborative Optimization of Distribution Routes and Speeds for Fresh Food UAVs Under Dual Objectives of Freshness and Energy Consumption

ZHAO Yiwei; HU Ting; ZUO Congjun

doi:10.19554/j.cnki.1001-3563.2026.07.024

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (7) : 203-212. DOI: 10.19554/j.cnki.1001-3563.2026.07.024

Collaborative Optimization of Distribution Routes and Speeds for Fresh Food UAVs Under Dual Objectives of Freshness and Energy Consumption

ZHAO Yiwei, HU Ting^*, ZUO Congjun

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Abstract

The work aims to quantify the conflicting "freshness-energy consumption" relationship and establish a collaborative optimization method for distribution routes and flight speeds to enhance the comprehensive benefits and operational flexibility of the system, so as to address the trade-off between freshness preservation and energy conservation induced by flight speed in UAV fresh food distribution. With the dual objectives of minimizing freshness loss costs and drone energy consumption, the flight speed was taken as a continuous decision variable in a dual-objective optimization model. A two-stage solution approach was developed. In Phase I, the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) was employed to optimize delivery route. In Phase Ⅱ, a distance-aware heuristic speed optimization algorithm was employed for each route. Simulations were conducted in MATLAB using small-scale (5 customers) and medium-scale (10 customers) cases. The Pareto frontier analysis revealed 40 and 73 non-dominated solutions in the 5- and 10-customer cases respectively, demonstrating a significant trade-off between freshness preservation and energy efficiency. Through Phase Ⅱ speed optimization, total costs were reduced by 19.1% and 9.6% compared with fixed-speed strategies, with optimized speeds exhibiting segmentally differentiated characteristics. This study validates the effectiveness of the proposed model in capturing the conflicting relationship. The two-stage optimization significantly lowers total cost, and the NSGA-Ⅱ algorithm can generate well-distributed Pareto optimal solutions, providing diversified operational strategies for decision-makers.

Key words

UAV logistics / fresh food delivery / multi-objective optimization / route-speed joint optimization / Non-dominated Sorting Genetic Algorithm II (NSGA-Ⅱ)

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ZHAO Yiwei, HU Ting, ZUO Congjun. Collaborative Optimization of Distribution Routes and Speeds for Fresh Food UAVs Under Dual Objectives of Freshness and Energy Consumption[J]. Packaging Engineering. 2026, 47(7): 203-212 https://doi.org/10.19554/j.cnki.1001-3563.2026.07.024

References

[1] 马祖军, 王一然. 考虑生鲜农产品“最先一公里”损耗的预冷站选址定容[J]. 中国管理科学, 2024, 32(2): 315-323.
MA Z J, WANG Y R.Optimal Location and Capacity of Pre-Cooling Facilities Considering the First-Mile Loss of Fresh Agri-Products[J]. Chinese Journal of Management Science, 2024, 32(2): 315-323.
[2] 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.
[3] 刘思远, 陈天恩, 陈栋, 等. 时变多车型下的生鲜农产品配送路径优化模型[J]. 智慧农业(中英文), 2021, 3(3): 139-151.
LIU S Y, CHEN T E, CHEN D, et al.Time-Varying Heterotypic-Vehicle Cold Chain Logistics Distribution Path Optimization Model[J]. Smart Agriculture, 2021, 3(3): 139-151.
[4] 马佳, 李楚连, 李桐言. 考虑新鲜度的生鲜冷链物流同时取送货车辆路径及求解算法[J]. 包装工程, 2025, 46(13): 269-279.
MA J, LI C L, LI T Y.Simultaneous Pickup-Delivery Vehicle Routing Problem for Fresh Cold Chain Logistics Considering Freshness and Solution Algorithm[J]. Packaging Engineering, 2025, 46(13): 269-279.
[5] DI PUGLIA PUGLIESE L, GUERRIERO F, SCUTELLÁ M G. The Last-Mile Delivery Process with Trucks and Drones under Uncertain Energy Consumption[J]. Journal of Optimization Theory and Applications, 2021, 191(1): 31-67.
[6] 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.
LIN Y, LYU 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.
[7] 涂斌斌, 李美兰, 王飞. “双碳”背景下生鲜产品冷链配送路径优化[J]. 沈阳大学学报(自然科学版), 2025, 37(6): 496-504.
TU B B, LI M L, WANG F.Optimization of Cold Chain Distribution Routes for Fresh Products under “Carbon Peaking and Carbon Neutrality” Context[J]. Journal of Shenyang University (Natural Science), 2025, 37(6): 496-504.
[8] 江云倩, 杨慧敏, 彭程, 等. 考虑碳排放和时间窗的冷链物流配送路径优化研究[J]. 包装工程, 2024, 45(3): 262-268.
JIANG Y Q, YANG H M, PENG C, et al.Optimization of Cold Chain Logistics Distribution Route Considering Carbon Emission and Time Window[J]. Packaging Engineering, 2024, 45(3): 262-268.
[9] 周鲜成, 蒋涛营, 贺彩虹, 等. 冷链物流配送的绿色车辆路径模型及其求解算法[J]. 中国管理科学, 2023, 31(12): 203-214.
ZHOU X C, JIANG T Y, HE C H, et al.Green Vehicle Routing Model and Its Solution Algorithm in Cold-Chain Logistics Distribution[J]. Chinese Journal of Management Science, 2023, 31(12): 203-214.
[10] 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.
[11] 付朝晖, 李君宇, 刘长石. 山区生鲜物流卡车-无人机联合集货路径规划[J]. 计算机工程与应用, 2025, 61(14): 332-342.
FU Z H, LI J Y, LIU C S.Collaborative Truck-Drone Routing for Mountainous Fresh Produce Collection[J]. Computer Engineering and Applications, 2025, 61(14): 332-342.
[12] 李军涛, 周小蝶, 蒋佳佳, 等. 卡车-无人机协同配送生鲜产品多目标优化[J/OL]. 复杂系统与复杂性科学, 2025: 1-11[2025-12-08]. https://link.cnki.net/urlid/37.1402.N.20250929.0949.002.
LI J T, ZHOU X D, JIANG J J, et al. Multi-objective Optimization of Truck-UAV Collaborative Delivery for Fresh Products[J/OL]. Complex Systems and Complexity Science, 2025: 1-11[2025-12-08]. https://link.cnki.net/urlid/37.1402.N.20250929.0949.002.
[13] LI Z L, LI S, LU J, et al.Air Route Network Planning Method of Urban Low-Altitude Logistics UAV with Double-Layer Structure[J]. Drones, 2025, 9(3): 193.
[14] SHE R F, OUYANG Y F.Efficiency of UAV-Based Last-Mile Delivery under Congestion in Low-Altitude Air[J]. Transportation Research Part C: Emerging Technologies, 2021, 122: 102878.
[15] 傅惠, 崔煜, 赵佳虹, 等. 低空无人机物流配送研究与应用综述[J]. 工业工程, 2025, 28(1): 9-21.
FU H, CUI Y, ZHAO J H, et al.A Review of Research and Applications in Low-Altitude Logistics and Delivery Using Unmanned Aerial Vehicles[J]. Industrial Engineering Journal, 2025, 28(1): 9-21.
[16] YU Z.Assessment of UAV-Based Low-Altitude Logistics in Urban Supply Chains: Slope-Intercepted Grey Incidence Analysis[J]. Grey Systems: Theory and Application, 2025, 15(4): 881-895.
[17] ZHAO Y G, ZHANG M, MUJUMDAR A S, et al.AI-Based Low-Altitude Delivery Fresh Food Supply Chain: Research Progress and Trends[J]. Trends in Food Science & Technology, 2025, 161: 105056.
[18] 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.