Truck–Drone Collaborative Delivery of Emergency Supplies Considering Demand Priority Under Flood Disasters

Flood disasters often disrupt transportation networks and significantly increase the complexity of emergency relief distribution, threatening community sustainability. Existing truck–drone collaborative delivery studies mainly emphasize efficiency improvement while neglecting variations in demand urgency among affected areas, limiting their ability to balance routing efficiency and emergency response priorities. To address this issue, a truck–drone collaborative emergency relief distribution model that incorporates demand priority is proposed. A demand-point priority evaluation framework is first established based on population density, infrastructure damage, and regional economic importance. The entropy weight–TOPSIS method is employed to determine the comprehensive priority score of each demand node. A priority-delay penalty mechanism is then incorporated into the routing process to coordinate demand urgency with delivery efficiency. Accordingly, a bi-objective optimization model is formulated to minimize the total delivery completion time and maximize the comprehensive priority score, and an improved non-dominated sorting genetic algorithm II (INSGA-II) with an external archive is developed to solve the proposed model. A case study based on the May 2026 flood disaster in Shangyou County, Ganzhou, China, covering 15 demand nodes, is carried out. Compared with standard NSGA-II and SPEA2, INSGA-II shortens computational runtime by 9.50% and 29.86% respectively, and generates more diversified and evenly distributed Pareto solutions. Under compromise, priority-oriented and time-oriented decision-making scenarios, INSGA-II raises comprehensive priority scores by 7.27%, 5.26%, 14.09% versus standard NSGA-II and by 5.36%, 4.05%, 1.80% versus SPEA2, while maintaining competitive delivery-time performance. The proposed method provides effective decision support for emergency resource allocation and truck–drone collaborative emergency logistics in flood disasters.