Xiaolai Ma

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.

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.

Logistics network assignment problem is an important research field on logistics optimization. According to user equilibrium theory, The equilibrium assignment framework of logistics network is a basic problem for it. The framework structure of logistics network consisted of carriers, logistics service providers, the shippers, and the modified wardrop user equilibrium of logistics network is proposed. Based on the modified UE condition, the optimum condition/equilibrium status of carriers, logistics service providers and the shippers in the equilibrium situation are analyzed.

In the process of actual operation, the logistics chain is under stochastic conditions. There are two stochastic conditions in the logistics chain, the random early/delay and random sliding value. The sliding of t' of the random early/delay sliding obeys normal distribution, which parameters are μ_t' and σ²_t'(σ_t' > 0). The sliding value Δt still obeys normal distribution, which parameters are μΔt_e and σ²Δt_e(σΔt_e > 0) or μΔt_d and σ²Δt_d(σΔt_d > 0). Then, the formulation of the generalized cost at the i-th level in the logistics chain under random state condition is presented. Finally, a numerical example is given to verify the feasibility of the method.

There are several paths or nodes to be selected to form a useful and efficient logistics chain. But how to decide which path or node would be selected is an issue in the field of logistics planning and optimization. The issue can be described as mixed-integer programming. And the model can be settled by some exact algorithm, such as the branch and bound method, or random-heuristic search method when the dimensions of the problem are small. But when the dimensions of the problem are large, the problem belongs to NP-complete problem. A genetic algorithm is designed to settle the model. The main impact facts of the algorithm, such as coding, representation of the data, population size, and fitness function, are analyzed.