Mixed Integer Programming ( MIP ) for UAV Logistics Path Optimization: Charging Station Location and Multi‐Objective Distribution Collaborative Decision‐Making

Large‐scale UAV logistics deployment faces critical challenges from dynamic energy consumption in complex terrains, disconnected optimization of charging infrastructure and route planning, and multi‐objective conflicts. This study proposes a three‐dimensional “energy‐route‐objective” collaborative framework integrating charging station site selection, dynamic routing, and multi‐objective decision‐making via mixed integer programming (MIP). A two‐level optimization model is constructed: the upper level minimizes station construction costs while maximizing service coverage using discrete location variables; the lower level incorporates hyperspectral remote sensing‐derived terrain adaptive coefficients to quantify elevation/electromagnetic impacts on energy consumption, embedding dynamic constraints and time window penalties. A parallel adaptive large neighborhood search (PALNS) algorithm simultaneously optimizes operating costs, delivery timeliness, and demand fairness. Innovations include a Pareto archiving mechanism with dynamic damage‐repair for multi‐objective convergence; electromagnetic‐interference‐aware energy modeling; and spatiotemporal coupling of station utilization with route traffic. Validation across China's Hengduan Mountains and Jining City logistics networks demonstrates 31% reduction in operating costs; charging station utilization standard deviation decreased from 0.37 to 0.15; 93.8% on‐time delivery rate achieved; PALNS attained 0.87 hypervolume in 285 s (17.6% faster than NSGA‐II); and 89.7% routing success rate in complex terrain. The framework resolves resource‐load mismatches by dynamically synchronizing infrastructure planning and routing, while the terrain‐adaptive module enhances robustness in mountainous areas. The multi‐objective weighting function effectively balances timeliness deviation and cost elasticity. This provides a global optimization paradigm for UAV logistics networks with methodological implications for smart city energy scheduling. Future work will integrate weather forecasting and battery degradation models, and explore quantum computing acceleration.