Truck–Drone Collaborative Location-Routing Optimization for Emergency Supplies Considering Flood Impact Evolution and Stage-Specific Response

Flood disasters evolve through distinct stages, and emergency supply demand shifts accordingly, rendering static post-disaster distribution plans inadequate for stage-specific rescue needs. This study develops a multi-stage location-routing optimization model that integrates flood impact evolution with truck–drone collaborative delivery. A flood impact evolution model is first established to describe population transitions among the normal, mildly affected, severely affected, and recovery states. The emergency response period is accordingly divided into the initial, middle, and late stages. Stage-specific time–cost coefficients are then introduced to convert delivery time into equivalent cost, reflecting the varying urgency across stages. To handle the model’s dynamic nature, model predictive control is embedded into an improved salp swarm algorithm (MPC-ISSA), enabling rolling updates of distribution decisions as flood conditions change. A case study of the 2021 Zhengzhou urban flood reveals that center activation and routing patterns differ markedly across stages, with all four distribution centers activated during the high-impact middle stage. The MPC-ISSA outperforms GA, PSO, DE, and RCGA in all three stages, lowering the total objective value by an average of 6.87%, 13.82%, and 10.45%, respectively. Relative to truck-only delivery, the collaborative mode trims total delivery time by 51.58%, 10.75%, and 10.71% across the three stages. Sensitivity analysis further verifies that the stage division holds stable under ±10% parameter perturbations, and the rolling framework absorbs minor boundary shifts without degrading plan reliability. These findings confirm the model’s effectiveness and the value of truck–drone collaboration for time-critical flood emergency response.