Volume 43 Issue 3
Jun.  2025
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Article Navigation >  Journal of Transport Information and Safety  > 2025  >  43(3): 128-140
REN Xinhui, YU Fang. A Model for Drone Urban Delivery Scheduling Based on CPSS[J]. Journal of Transport Information and Safety, 2025, 43(3): 128-140. doi: 10.3963/j.jssn.1674-4861.2025.03.012
Citation: REN Xinhui, YU Fang. A Model for Drone Urban Delivery Scheduling Based on CPSS[J]. Journal of Transport Information and Safety, 2025, 43(3): 128-140. doi: 10.3963/j.jssn.1674-4861.2025.03.012
shu

A Model for Drone Urban Delivery Scheduling Based on CPSS

doi: 10.3963/j.jssn.1674-4861.2025.03.012
  • 1.

    Economics and Management College, Civil Aviation University of China, Tianjin 300300, China

  • 2.

    Transportation Science and Engineering College, Civil Aviation University of China, Tianjin 300300, China

  • Received Date: 2024-11-13
    Available Online: 2025-10-11
    Abstract
  • Unmanned aerial vehicle (UAV) urban logistics improves delivery efficiency but also brings risks such ascrashes, privacy violations, and noise pollution. To balance UAV logistics efficiency with social value, a cyber-physical-social system (CPSS) framework is used to analyze UAV urban logistics delivery scenarios. The framework investigates the impact of physical and social systems on UAV delivery paths and scheduling, converting these factorsinto information system support. A UAV urban logistics scheduling model is developed to minimize delivery costs, carbon emissions, and third-party risks. The model considers privacy protection layers, no-fly zones, third-party riskassessment, noise impact measurement, andpotential air conflict resolution strategies. A multi-objective A* algorithm is designed to find the Pareto optimal solution for path planning. An improved genetic algorithm is applied forscheduling, identifying repeated path segments among customers and setting delay times to resolve potential air conflicts. A case study validation is conducted on the flight environment within a 5 km radius of the Shenzhen GalaxyWorld commercial district.The results show that the multi-objective A* algorithm requires only 0.03 seconds, demonstrating superior efficiency compared to the 54.29 seconds required by the multi-objective labeling algorithm. Theimproved genetic algorithm completes the solution in 2.16 seconds, outperforming the CPLEX solver, which fails tosolve the problem within a reasonable time frame. Compared to cost-only paths, the multi-objective A* algorithm reduces third-party risk by 18.71%, while increasing energy costs and carbon emissions by 7.21%. The optimal UAVcruising altitude is 30 m for outbound flights and 60 m for return flights. The UAV conflict resolution rate reaches100%. The customer scale has a positive effect on all indicators, while the number of customer groups with repeatedpaths and the time-window penalty cost are mainly affected by the customer distribution type.

     

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