In context
As low-altitude economies expand, drone delivery has become a key application, yet the high initial investment and idle risks hinder widespread adoption. Shared drone platforms emerged to reduce costs, but effective scheduling under dynamic order flows remained a challenge, prompting research into time-flexible scheduling and advanced optimization algorithms.
What was reported
Researchers from the Civil Aviation University of China proposed a shared drone delivery scheduling model that integrates a full time flexibility index, allowing order service times to be advanced or delayed within limits. This enables intelligent grouping of delivery tasks to better utilize idle time between orders.
To solve the model's strong integer constraints, they developed an improved alpha evolution algorithm featuring a feasible solution construction mechanism based on full order traversal, a rounding-based random step update rule for discrete search, and a periodic boundary restriction operator to maintain population diversity.
Simulations using real Meituan order data showed that when 100% of orders are fully time-flexible, average daily revenue increases by 1,237.42 yuan (+23.6%), equipment payback period shortens by 75.99 days (-19.21%), and order service rate improves by 1.63% compared to rigid time windows. The study confirms a positive correlation between time-flexible order proportion and operational benefits.
Why it mattered
This work provides decision support for logistics platforms to build hybrid scheduling systems combining rigid guarantees with elastic optimization. The algorithm framework is extendable to instant delivery and emergency logistics, offering a pathway to improve drone utilization and economic viability in last-mile delivery.
"The research confirms that there is a significant positive correlation between the proportion of time flexible orders and operational benefits."
Source: 《机器人》期刊 (robot.sia.cn) · Published 2025-06-09 · “基于改进阿尔法进化算法的共享无人机配送调度优化”