Truck–Drone Collaborative Distribution Path Planning Based on Multi-Objective Optimization: Balance of Economy, Safety and Environment

Existing research on truck–drone collaborative delivery-path planning predominantly focuses on single or partial optimization objectives, lacking holistic integration of economic, safety, and environmental benefits. Rooted in the Triple Bottom Line (TBL) framework, this study proposes the ESE-VRP-mD model alongside an Improved Multi-Objective Particle Swarm Optimization (IMOPSO) algorithm for multi-objective route optimization. Moving beyond conventional cost-driven optimization, the proposed model incorporates transportation expenditures, drone crash hazards, noise disturbances, and operational carbon emissions into a unified multi-benefit analytical framework. Noise exposure is quantified following point-source sound propagation principles; crash risk is evaluated by synthesizing crowd density and drone maneuvering performance; cost metrics cover unit delivery expenses for all transport carriers. Computational experiments demonstrate that IMOPSO yields superior Pareto-optimal solution distribution compared with NSGA-II and standard MOPSO. Heterogeneous-drone delivery outperforms homogeneous-drone schemes in environmental adaptability and cost-risk trade-offs, with drone flight endurance and payload capacity acting as core cost-efficiency constraints. The developed model and algorithm are capable of cutting operational costs, mitigating safety hazards, and curbing environmental footprints. They can be directly embedded into logistics scheduling systems to assist dispatchers in generating practical trade-off delivery schemes under diverse operational preferences.