Distribution Scheduling Optimization of Shared Drone Based on the Improved Alpha Evolution Algorithm

Drone distribution is the core application scenario of low-altitude economy. Its sharing mode not only reduces the initial investment of logistics enterprises, but also faces the triple operation challenges, including dynamic balance of order income, compression of equipment investment recovery cycle, and improvement of customer service rate. In order to break through the limitations of the existing scheduling strategies in the utilization efficiency of space-time resources, this paper innovatively constructs a shared drone distribution scheduling model integrating the complete time flexibility index, and realizes the intelligent grouping of distribution tasks by quantifying the elastic characteristics of the order time window. Aiming at the strong integer constraint of the model, an improved alpha evolution algorithm is proposed. A feasible solution construction mechanism based on full order traversal is designed to ensure the feasibility of the initial population. The random step size updating rule of rounding is adopted to improve the local search accuracy while ensuring the discreteness of solution space. The periodic boundary restriction operator is introduced to maintain the population diversity through periodic mapping. The simulation experiment based on the real order data of Meituan takeout shows that when the full-time flexible order accounts for 100%, the average daily income can be increased by 1237.42 yuan (+23.6%), the equipment return cycle can be shortened by 75.99 days (-19.21%), and the order service rate can be increased by 1.63%, compared with the rigid time window scenario. The research confirms that there is a significant positive correlation between the proportion of time flexible orders and operational benefits, which provides decision support for the logistics platform to build a hybrid scheduling system of "rigid support-elastic optimization", and its algorithm framework can be extended to multi-task scheduling scenarios such as instant distribution and emergency logistics.