In the context of sustainable logistics planning, carbon emission costs have become a critical factor influencing distribution decisions. Meanwhile, the time-dependent characteristics of urban road networks and simultaneous pickup–delivery operations present significant challenges to vehicle routing problems (VRPs). This study addresses a time-dependent vehicle routing problem with simultaneous pickup–delivery and time windows (TDVRPSPDTW). Fuel consumption and carbon emission costs are quantified using a comprehensive emission model, while time-dependent network conditions, simultaneous pickup–delivery demands, and time window constraints are integrated into a unified modeling framework. To solve this NP-hard problem, an improved ant colony optimization (IACO) algorithm is developed by incorporating adaptive large neighborhood search to enhance solution diversity and convergence efficiency. Computational experiments are conducted using internationally recognized VRPSPDTW benchmark datasets and newly constructed TDVRPSPDTW instances, together with sensitivity analyses under varying traffic conditions, time window flexibility, and delivery strategies. The results indicate that the proposed IACO effectively addresses the TDVRPSPDTW. Comparing ant colony optimization with local search (ACO-LS), the IACO achieves a maximum reduction of 11.78% in total distribution cost. Furthermore, relative to the conventional separate pickup–delivery strategy, the simultaneous pickup–delivery mode reduces total distribution cost and carbon emission cost by 49.96% and 53.48%, respectively.

Inspired by the practical need for reliable drone-assisted logistics under varying weather conditions, this study investigates the vehicle–drone collaborative routing problem with weather constraints and time windows. The objective is to minimize the total delivery cost, including vehicle fixed costs, vehicle travel costs, drone flight costs, and time window penalty costs. To capture the impact of weather conditions on drone operations, a wind-speed-dependent dynamic flight speed function is introduced. A mathematical model is formulated, and an adaptive large neighborhood search algorithm integrated with genetic operations is proposed to enhance both local search efficiency and global exploration capability. Computational experiments on benchmark instances demonstrate that the proposed algorithm obtains high-quality solutions across different problem scales. Compared with the adaptive large neighborhood search algorithm and the improved genetic algorithm, the proposed approach reduces the optimal total delivery cost by an average of 4% and 2%, respectively. Sensitivity analysis further shows that increasing wind speed levels and the proportion of no-fly periods reduces the number of drone service tasks and increases total system cost, highlighting the significant impact of weather conditions on vehicle–drone collaborative delivery systems.