Multi-Stage Cross-Regional Emergency Network Design and Dynamic Optimization under Disaster Spatiotemporal Evolution Characteristics

Objective To address the spatiotemporal dynamic evolution of regional disaster losses in sudden disaster management, as well as the resulting high uncertainty in post-disaster emergency demand and road segment capacity.

Method This paper proposes an integrated intelligent prediction and stochastic optimization framework for multi-stage cross-regional emergency response network design, namely the smart predict-then-optimize for multi-stage cross-regional emergency response network （SPO-MCERN）, to coordinate pre-disaster preparedness and post-disaster response decisions. First, to overcome the limitations of traditional statistical methods in capturing the complex spatiotemporal evolution of disaster losses, a hybrid deep learning prediction model, Conv-MHSA, is developed by integrating one-dimensional convolutional operations （1D-Conv） with a multi-head self-attention （MHSA） mechanism. Based on the constructed high-dimensional regional disaster feature set, the model employs an odd-even feature separation strategy for effective dimensionality reduction, thereby achieving high-accuracy prediction of regional disaster losses. Secondly, based on the prediction outputs, a dynamic assessment mechanism incorporating regional correction and seasonal response is designed to quantify post-disaster dynamic demand and road segment capacity. Finally, the SPO-MCERN model is reformulated using the sample average approximation （SAA） method, and a hybrid stochastic optimization algorithm combining Monte Carlo simulation and SAA is proposed.

Result Based on the historical data of earthquake disasters in China, Conv-MHSA demonstrates significant advantages in terms of prediction accuracy, robustness and overall performance. The case study of the emergency response network design in Gansu Province shows that the SPO-MCERN framework enables the network design and transportation costs to remain optimal under the conditions of temporal and spatial evolution.

Conclusion Compared with traditional approaches, the coordinated predict-then-optimize decision-making mechanism reduces redundant facility and material deployment costs by 21.6%, while correspondingly reducing post-disaster transportation and procurement costs by 25.9%. It can provide scientific support for cross-regional emergency decision-making in disaster scenarios.