ABSTRACT In this paper, we study the fresh product first‐mile pickup problem, which allows the vehicle and drone to visit the same retrieval node. The problem is formulated as a mixed linear programming model with two objectives, which minimize the operational cost and completion time. An adaptive large neighborhood search (ALNS) algorithm is designed to solve the model. Specifically, an adaptive weighted normalized objective function is introduced to intelligently balance the two core objectives of transportation time and total cost. In addition, the algorithm incorporates the simulated annealing acceptance criterion together with a makespan‐prioritized forced acceptance strategy, which effectively prevents the search process from being trapped in local optima. Numerical experiments are conducted to verify the effectiveness of the algorithm. The experimental results show that ALNS can obtain optimal (or near‐optimal) solutions for small‐scale instances and is efficient in generating satisfactory solutions for large‐scale instances within an acceptable computational time. Taking the instance of the Yangcheng Lake hairy crab, the vehicle‐drone collaborative pickup scheme is compared with the vehicle‐only pickup scheme. The results demonstrate that the vehicle‐drone collaborative pickup scheme has advantages in both overall operational cost and completion time. Moreover, based on the practical instance, a sensitivity analysis is conducted to investigate the impact of different drone numbers and flight range on the efficiency of vehicle‐drone collaborative pickup.
Liu et al. (Sun,) studied this question.