In recent years, there have been more frequent disasters occurred in China, which pose significant threats to the lives and property of the people. To cope with the increasing complexity and severity of disasters, decision-makers need to store and dispatch emergency supplies rationally based on the real situation. Current studies on regional dispatch considering multiple warehouses and demand points are insufficient, and the problems such as ‘who/how/how much to dispatch’ have not been well-answered. To solve these problems, this paper proposes three regional dispatching strategies (including strict administrative hierarchy supply dispatch, cross-administrative hierarchy supply dispatch and free and nearest supply dispatch strategies) based on a comprehensive summary of relevant case studies, then builds a multi-agent simulation model based on deprivation cost. A simulation experiment is conducted in Mengcheng County, Bozhou City, Anhui Province, and the result shows that when the regional demand is large in a short time, the free proximity strategy can minimize the total social logistics cost. On the contrary, when the regional demand is small, the differences of the total social cost among three strategies are small. In conclusion, our research suggests that, when facing severe disasters and catastrophes, governments should cooperate and coordinate on the dispatching of relief supplies. However, when facing normal disasters without the risk of life, the demand can be satisfied with the strict administrative strategy.

In the numerous terrorist attacks that have occurred worldwide, various terrorist organizations have shown a trend of collaborative cooperation, posing significant challenges to international counter-terrorism efforts. Based on the global terrorism database (GTD), this study constructs a terrorist organization cooperation evolution network from 121,074 terrorist attacks that occurred globally from 2001 to 2018 and conducts a time-series topological structure analysis. Based on the characteristics of terrorist organization cooperation, the network is divided into time slices of three years each to model the flow patterns of terrorist communities at multiple scales. The analysis shows that the robustness of the terrorist organization cooperation network has been continually strengthening over time, which is necessary to develop corresponding strategies to disrupt it. Focusing on the largest connected sub-network within the terrorist cooperation network, whose influence is continuously expanding, this study proposes a community structure-based neighborhood centrality index (CSNC) to measure the importance of nodes in the largest connected component. Experimental results demonstrate that the network disruption strategy based on CSNC, in the process of disintegrating the terrorist cooperation network from 2001 to 2018, achieved a 16.45% maximum reduction in the R value compared to benchmark strategies, proving that the CSNC-based disruption strategy can more effectively dismantle terrorist cooperation networks.

Identifying key segments within road networks is crucial for selecting repair objectives and optimizing repair sequences during the post-disaster recovery phase. Traditional methods for identifying key segments have not fully explored the interactions between multiple segments, particularly the significance of studying road network vulnerability under simultaneous disruptions of multiple links. To tackle this issue, this study introduces a machine learning model called transportation graph attention networks for criticality analysis (TGAT) to identify key road segments when facing multiple disruptions. This model is trained on data samples that include scenarios of multiple segment failures, utilizing the graph attention network to evaluate the influence weights between segments and calculating the criticality of each segment based on these weights. The model, trained using mean squared error as the loss function, is capable of identifying segments that play a crucial role in the performance of the road network. Taking the Kunshan City road network as an example, this paper compares the effectiveness of the TGAT method with three other methods:Degree centrality, weighted betweenness centrality, and eigenvector centrality, in optimizing repair sequences during the post-recovery phase. Experimental results indicate that the TGAT method is more effective in identifying key segments within the road network compared to the other three methods, and the repair sequence optimized using TGAT further enhances the repair performance of the road network.

Exploring the heterogeneity characteristics of nodes and edges in emergency logistics networks has a great impact on the location-allocation optimization problem throughout the entire emergency system. First, for the disaster preparedness stage, based on the complex network theory, this paper selects the node vulnerability indicators to characterize the heterogeneity of various emergency reserve facilities. The vulnerability indicators of transportation connected edges among emergency reserve facilities, and between emergency reserve facilities and disaster-affected demand areas, are also incorporated into the emergency location-allocation optimization decision-making. And a multi-objective location-allocation model is formulated, for comprehensively balancing vulnerability, efficiency and cost-effectiveness in the emergency logistics network. Then, taking the emergency reserve network covering 13 prefecture-level cities in Jiangsu Province as a case example, an NSGA-II algorithm is designed to solve the constructed model and perform a numerical simulation. Not only the sensitivity analysis for the parameters is analyzed, but also the comparative analysis is discussed. The simulation results show that the location-allocation optimization problem for heterogeneous emergency reserve facilities from the perspective of vulnerability, not only performs well in cost-effectiveness and allocation efficiency, but also significantly improves the robustness and resilience of the entire emergency logistics network. This work provides some useful managerial insights for emergency decision-makers to make an effective disaster preparedness plan.