Truck-drone collaborative delivery route optimization with carbon cost under normal-emergency dual-use

Urban freight systems in megacities must simultaneously address carbon reduction targets and emergency preparedness requirements. This study develops a systems-oriented optimization framework for truck-drone collaborative delivery that integrates both objectives. Using Chengdu as a case study, we propose a dual-mode routing model that explicitly incorporates carbon costs as a carbon tax into the objective function. The model is solved using an Adaptive Large Neighborhood Search algorithm enhanced with Q-learning (ALNS-RL), featuring carbon-sensitive destruction and battery-constrained repair operators. We calibrate the model using Chengdu’s emission inventory across nine transport modes (2020–2025) and simulate it under carbon tax scenarios of 80, 150, and 300 CNY/ton. Results show that the drone usage ratio increases from 10.3% to 25.0% as the carbon tax rises, with a marginal incentive threshold near 150 CNY/ton. Under emergency mode, drone usage increases by 20%, while total cost and carbon emissions decrease by 4.3% and 4.2%, respectively. Overall, the proposed method reduces total cost by 31.0% and carbon emissions by 29.2% relative to a pure truck baseline. This study provides a practical decision-support framework for designing low-carbon and resilient urban logistics systems in megacities.