Time-Efficient Routing and Speed Control for Truck Drone Delivery Under Non-Linear Energy Constraints

Existing truck–drone collaborative routing models predominantly assume fixed flight speeds, overlooking the non-linear coupling among speed, payload, and energy consumption, which limits urban delivery efficiency. To bridge this gap, this paper proposes the multiple flying sidekick traveling salesman problem with variable drone speed (mFSTSP-VDS). Formulating drone cruising speed as a continuous variable under strict non-linear energy constraints, we design a hybrid algorithm (ALNS-SA-VND) to jointly optimize routing, task allocation, and speed. Empirical analysis of Wuhan’s road network demonstrates the VDS strategy’s robustness. Specifically, VDS reduces the system makespan by up to 17.5% compared to rigid maximum-speed strategies, with consistent stability across varying load scenarios. By adaptively trading permissible battery capacity for temporal synchronization, VDS effectively mitigates unnecessary truck waiting times at rendezvous nodes. This study quantitatively validates the impact of sortie-specific speed adaptation on time efficiency, providing an exploratory theoretical baseline for tactical-level planning in smart logistics networks.