Supernetwork optimization method for low-altitude emergency supply scheduling considering extreme weather impacts

To improve the delivery efficiency of emergency supplies in disaster-stricken areas under extreme weather while balancing scheduling costs and risk control, the optimization of low-altitude flight paths and the collaboration of multi-platform resources were taken as key focal points, and a multi-objective optimization method for low-altitude emergency supply scheduling based on an improved supernetwork was studied. By considering the heterogeneity of air-ground networks and the collaborative characteristics of multi-level nodes under extreme weather, a supernetwork planning model for low-altitude emergency supply scheduling was constructed, with the selection of supply transportation paths, flight platform configuration, and transit node selection as decision variables, and the minimizations of total transportation cost, average response time, and system risk as three objective functions. In view of the difficulty in quantifying the vulnerability of transportation networks and the impact of extreme weather, an improved parameter calculation method was developed and combined with the extreme weather risk index to achieve an accurate evaluation of the supernetwork model. After the parameters were embedded into the model, the selections of emergency scheduling paths, transportation volumes, and transportation modes were optimized through collaborative computing capabilities. Based on the complexity of multi-level decision-making, a variational inequality transformation mechanism and an improved projection algorithm were designed for the model. The feasibility of the model was verified through a numerical example in an urban extreme weather scenario, and the optimal transportation paths, platform allocations, and supply flow schemes were output. Research results show that the model framework can effectively integrate multiple types of supplies, heterogeneous platforms, and multi-level node resources, and the improved projection algorithm can efficiently solve the supernetwork optimization problem. The output results of the numerical example show the optimal transportation volume allocation schemes for different supply categories corresponding to various platforms and links. On the premise of satisfying capacity and operational constraints, the optimization of cost, time, and risk objectives can be achieved, which confirms that the method possesses rapid response capability and practical application potential under extreme weather conditions.