Multi-objective Route Optimization for Truck and Drone Collaborative Delivery in Instant Logistics

KONG Xin; XIA Meijun

doi:10.19554/j.cnki.1001-3563.2026.09.032

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (9) : 306-320. DOI: 10.19554/j.cnki.1001-3563.2026.09.032

Green Packaging and Circular Economy

Multi-objective Route Optimization for Truck and Drone Collaborative Delivery in Instant Logistics

KONG Xin, XIA Meijun^*

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Abstract

To address the issues of customer perception biases and insufficient quantification of dynamic vehicle carbon emissions in instant logistics, the work aims to construct a collaborative delivery route optimization model for trucks and drones that balances low costs, low customer dissatisfaction, and green, low-carbon operations, achieving an optimal balance between service quality and environmental benefits in urban delivery. Firstly, customers were classified into three service types based on the accessibility of their locations to establish the principles of collaborative service. Secondly, a multi-objective optimization model was established with the objectives of minimizing total delivery cost, customer dissatisfaction, and carbon emissions by taking into account factors such as vehicle payload, soft and hard time window constraints, customer dissatisfaction, and carbon emissions. The NSGA-II algorithm was improved by introducing a conflict detection and resolution mechanism and integrating local search strategies to enhance its global optimization capability. Finally, the effectiveness of the model and algorithm was validated through performance testing, algorithm model comparisons, sensitivity analysis, and real-world empirical analysis. The algorithm yielded higher-quality solutions and could effectively handle the complex constraints of collaborative delivery. Compared to traditional truck delivery models, the collaborative delivery scheme reduced total delivery costs, customer dissatisfaction, and carbon emissions by 7.07%, 19.47%, and 24.84%, respectively. Real-world scenario analysis further validated the model's effectiveness. This work validates the applicability of the collaborative delivery model and the improved NSGA-II algorithm in on-demand logistics scenarios, effectively reducing customer dissatisfaction while simultaneously achieving cost reduction and efficiency gains in last-mile delivery, and balancing the synergistic improvement of low-carbon sustainability and user experience.

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multi-objective optimization / improved non-dominated sorting genetic algorithm / instant logistics / truck-drone collaborative delivery

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KONG Xin, XIA Meijun. Multi-objective Route Optimization for Truck and Drone Collaborative Delivery in Instant Logistics[J]. Packaging Engineering. 2026, 47(9): 306-320 https://doi.org/10.19554/j.cnki.1001-3563.2026.09.032

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