Low-Carbon Truck-Multi-Drone Collaborative Routing for Urban Cold-Chain Distribution with Perishability and Time-Varying Payloads
DOI:
https://doi.org/10.70088/9c49bf18Keywords:
cold-chain logistics, vehicle routing, truck-drone collaboration, multi-objective optimization, carbon emissions, perishabilityAbstract
Rapid grocery and fresh-food fulfilment requires delivery systems that reconcile operating cost, product freshness, service reliability, and environmental performance. Conventional truck-only routing is vulnerable to congestion and road closures, whereas stand-alone drones remain constrained by payload, endurance, launch and recovery requirements, and regulated airspace. This study formulates a multi-objective truck-multi-drone routing problem for urban cold-chain distribution incorporating simultaneous delivery and pickup, split service, soft time windows, ground blockages, no-fly arcs, truck-drone synchronization, exponential quality decay, and payload-dependent operational emissions. The three objectives minimize economic fulfilment cost, monetized freshness loss, and operational carbon dioxide equivalent emissions. A multi-objective adaptive large-neighborhood search (MO-ALNS) is developed around a bounded nondominated archive, crowding-based diversity control, adaptive operator selection, and problem-specific destroy and repair operators addressing high-loss customers, restriction boundaries, split-demand structures, safe air insertion, and launch-rendezvous synchronization. A Manhattan-calibrated scenario is used to evaluate the formulation. Relative to truck-only delivery, the collaborative configuration reduces total fulfilment cost by 18.70%, freshness-loss cost by 42.30%, operational emissions by 17.51%, and the time-window violation rate by 26.3 percentage points. Representative Pareto solutions expose a clear cost-freshness-carbon trade-off, while carbon-price scenarios indicate a shift toward higher drone participation and earlier unloading of high-demand customers. Against NSGA-II, MO-ALNS produces a 14.2% higher hypervolume and a 24.4% lower generational distance. Stress tests further demonstrate that the collaborative network contains severe congestion more effectively than truck-only routing, supporting payload-aware, restriction-aware routing as a practical basis for resilient low-carbon cold-chain planning.References
P. Bouman, N. Agatz, and M. Schmidt, "Dynamic programming approaches for the traveling salesman problem with drone," Networks, vol. 72, no. 4, pp. 528–542, 2018.
D. Schermer, M. Moeini, and O. Wendt, "A branch-and-cut approach and alternative formulations for the traveling salesman problem with drone," Networks, vol. 76, no. 2, pp. 164–186, 2020.
Q. M. Ha, Y. Deville, Q. D. Pham, and M. H. Hà, "On the min-cost traveling salesman problem with drone," Transportation Research Part C: Emerging Technologies, vol. 86, pp. 597–621, 2018.
A. Kumar, V. Prybutok, and V. K. R. Sangana, "Environmental implications of drone-based delivery systems: A structured literature review," Clean Technologies, vol. 7, no. 1, p. 24, 2025.
W. C. Chiang, Y. Li, J. Shang, and T. L. Urban, "Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization," Applied Energy, vol. 242, pp. 1164–1175, 2019.
E. Demir, T. Bektaş, and G. Laporte, "An adaptive large neighborhood search heuristic for the pollution-routing problem," European Journal of Operational Research, vol. 223, no. 2, pp. 346–359, 2012.
C. I. Hsu, S. F. Hung, and H. C. Li, "Vehicle routing problem with time-windows for perishable food delivery," Journal of Food Engineering, vol. 80, no. 2, pp. 465–475, 2007.
E. Demir, T. Bektaş, and G. Laporte, "A review of recent research on green road freight transportation," European Journal of Operational Research, vol. 237, no. 3, pp. 775–793, 2014.
A. Osvald and L. Z. Stirn, "A vehicle routing algorithm for the distribution of fresh vegetables and similar perishable food," Journal of Food Engineering, vol. 85, no. 2, pp. 285–295, 2008.
P. Amorim and B. Almada-Lobo, "The impact of food perishability issues in the vehicle routing problem," Computers & Industrial Engineering, vol. 67, pp. 223–233, 2014.
S. H. Chung, B. Sah, and J. Lee, "Optimization for drone and drone-truck combined operations: A review of the state of the art and future directions," Computers & Operations Research, vol. 123, p. 105004, 2020.
M. M. Solomon, E. K. Baker, and J. R. Schaffer, "Vehicle routing and scheduling problems with time window constraints: Efficient implementations of solution improvement procedures," in Vehicle Routing: Methods and Studies, vol. 16, 1988.
G. Attenni, V. Arrigoni, N. Bartolini, and G. Maselli, "Drone-based delivery systems: A survey on route planning," IEEE Access, vol. 11, pp. 123476–123504, 2023.
Y. Li, W. Yang, and B. Huang, "Impact of UAV delivery on sustainability and costs under traffic restrictions," Mathematical Problems in Engineering, vol. 2020, no. 1, p. 9437605, 2020.
F. A. T. Montané and R. D. Galvão, "Vehicle routing problems with simultaneous pick-up and delivery service," Opsearch, vol. 39, no. 1, pp. 19–33, 2002.
A. Baldisseri, C. Siragusa, A. Seghezzi, R. Mangiaracina, and A. Tumino, "Truck-based drone delivery system: An economic and environmental assessment," Transportation Research Part D: Transport and Environment, vol. 107, p. 103296, 2022.
K. Deb, A. Pratap, S. Agarwal, and T. A. M. T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.
E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and F. V. G. Da, "Performance assessment of multiobjective optimizers: An analysis and review," IEEE Transactions on Evolutionary Computation, vol. 7, no. 2, pp. 117–132, 2003.
Y. Li, H. Chen, and C. Prins, "Adaptive large neighborhood search for the pickup and delivery problem with time windows, profits, and reserved requests," European Journal of Operational Research, vol. 252, no. 1, pp. 27–38, 2016.
Y. Li, M. K. Lim, W. Xiong, X. Huang, Y. Shi, and S. Wang, "An electric vehicle routing model with charging stations consideration for sustainable logistics," Industrial Management & Data Systems, vol. 124, no. 3, pp. 1076–1106, 2024.
S. Zhang, C. Guan, Y. Qiu, and N. Wu, "Multi-objective route optimization of urban cold chain distribution using electric and diesel powered vehicles," Research in Transportation Business & Management, vol. 49, p. 100969, 2023.
F. Y. Vincent, P. H. Hsu, and S. W. Lin, "The cold chain vehicle routing problem in omni-channel retailing," Expert Systems with Applications, p. 132564, 2026.
L. Zhang, Y. Gao, Y. Sun, T. Fei, and Y. Wang, "Application on cold chain logistics routing optimization based on improved genetic algorithm," Automatic Control and Computer Sciences, vol. 53, no. 2, pp. 169–180, 2019.
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Copyright (c) 2026 Fangyan Ma, Longcan Wang (Author)
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